dataset <- read.delim("raw_data/Figure3B.txt", stringsAsFactors = FALSE, header = F)
mms_levels <- dataset[1,-1]
genotype_levels <- dataset$V1[!(grepl("OP|NT", dataset$V1))]
olaparib_levels <- unique(dataset$V1[(grepl("OP|NT", dataset$V1))])
dataset <- dataset[grepl("OP|NT", dataset$V1),]
colnames(dataset) <- c("Treatment",mms_levels)
dataset[,-1] <- lapply(dataset[,-1], as.integer)
n_reps <- nrow(dataset)/length(genotype_levels)/length(olaparib_levels)
dataset$Treatment <- factor(dataset$Treatment)
dataset$genotype <- factor(rep(genotype_levels, each = (n_reps*length(genotype_levels))))
dataset$Experiment <- factor(rep(paste0("exp", 1:(nrow(dataset)/length(olaparib_levels))),
each=length(olaparib_levels)))
dataset$UID <- factor(paste0("uid", 1:(n_reps*length(genotype_levels)*length(olaparib_levels))))
# wide format
kable(dataset, row.names = F)
| Treatment | NT | MMS_0,0003 | MMS_0,0006 | MMS_0,001 | genotype | Experiment | UID |
|---|---|---|---|---|---|---|---|
| NT | 2272 | 1946 | 1676 | 1422 | WT | exp1 | uid1 |
| OP_30 nM | 2050 | 1777 | 1512 | 1228 | WT | exp1 | uid2 |
| OP_300 nM | 1876 | 1568 | 1232 | 1044 | WT | exp1 | uid3 |
| OP_3000 nM | 1348 | 1221 | 888 | 568 | WT | exp1 | uid4 |
| NT | 1224 | 1110 | 1051 | 920 | WT | exp2 | uid5 |
| OP_30 nM | 1140 | 1020 | 892 | 696 | WT | exp2 | uid6 |
| OP_300 nM | 1080 | 808 | 596 | 425 | WT | exp2 | uid7 |
| OP_3000 nM | 952 | 652 | 432 | 245 | WT | exp2 | uid8 |
| NT | 1608 | 1553 | 1295 | 1121 | WT | exp3 | uid9 |
| OP_30 nM | 1542 | 1264 | 1120 | 838 | WT | exp3 | uid10 |
| OP_300 nM | 1208 | 920 | 755 | 488 | WT | exp3 | uid11 |
| OP_3000 nM | 850 | 732 | 600 | 385 | WT | exp3 | uid12 |
| NT | 1004 | 756 | 615 | 388 | PARP1 KO | exp4 | uid13 |
| OP_30 nM | 960 | 740 | 540 | 324 | PARP1 KO | exp4 | uid14 |
| OP_300 nM | 896 | 670 | 522 | 244 | PARP1 KO | exp4 | uid15 |
| OP_3000 nM | 728 | 630 | 477 | 212 | PARP1 KO | exp4 | uid16 |
| NT | 1150 | 955 | 677 | 422 | PARP1 KO | exp5 | uid17 |
| OP_30 nM | 1030 | 888 | 544 | 398 | PARP1 KO | exp5 | uid18 |
| OP_300 nM | 900 | 857 | 512 | 212 | PARP1 KO | exp5 | uid19 |
| OP_3000 nM | 754 | 688 | 410 | 156 | PARP1 KO | exp5 | uid20 |
| NT | 988 | 756 | 621 | 328 | PARP1 KO | exp6 | uid21 |
| OP_30 nM | 1020 | 678 | 554 | 222 | PARP1 KO | exp6 | uid22 |
| OP_300 nM | 828 | 655 | 468 | 128 | PARP1 KO | exp6 | uid23 |
| OP_3000 nM | 668 | 545 | 365 | 120 | PARP1 KO | exp6 | uid24 |
| NT | 2336 | 1756 | 1396 | 700 | ALC1 KO | exp7 | uid25 |
| OP_30 nM | 1944 | 896 | 740 | 121 | ALC1 KO | exp7 | uid26 |
| OP_300 nM | 776 | 616 | 441 | 101 | ALC1 KO | exp7 | uid27 |
| OP_3000 nM | 405 | 324 | 112 | 38 | ALC1 KO | exp7 | uid28 |
| NT | 1760 | 1540 | 966 | 588 | ALC1 KO | exp8 | uid29 |
| OP_30 nM | 952 | 710 | 450 | 120 | ALC1 KO | exp8 | uid30 |
| OP_300 nM | 422 | 368 | 320 | 77 | ALC1 KO | exp8 | uid31 |
| OP_3000 nM | 332 | 210 | 102 | 62 | ALC1 KO | exp8 | uid32 |
| NT | 1968 | 1568 | 999 | 652 | ALC1 KO | exp9 | uid33 |
| OP_30 nM | 911 | 896 | 620 | 210 | ALC1 KO | exp9 | uid34 |
| OP_300 nM | 711 | 669 | 410 | 141 | ALC1 KO | exp9 | uid35 |
| OP_3000 nM | 321 | 114 | 95 | 40 | ALC1 KO | exp9 | uid36 |
| NT | 2424 | 1855 | 1355 | 751 | ALC1 KO PARP1 KO | exp10 | uid37 |
| OP_30 nM | 2400 | 1755 | 1080 | 422 | ALC1 KO PARP1 KO | exp10 | uid38 |
| OP_300 nM | 2322 | 1641 | 850 | 355 | ALC1 KO PARP1 KO | exp10 | uid39 |
| OP_3000 nM | 2188 | 1555 | 742 | 288 | ALC1 KO PARP1 KO | exp10 | uid40 |
| NT | 1677 | 1489 | 1023 | 612 | ALC1 KO PARP1 KO | exp11 | uid41 |
| OP_30 nM | 1512 | 1255 | 741 | 355 | ALC1 KO PARP1 KO | exp11 | uid42 |
| OP_300 nM | 1155 | 1099 | 612 | 355 | ALC1 KO PARP1 KO | exp11 | uid43 |
| OP_3000 nM | 922 | 958 | 588 | 244 | ALC1 KO PARP1 KO | exp11 | uid44 |
| NT | 1366 | 1120 | 785 | 433 | ALC1 KO PARP1 KO | exp12 | uid45 |
| OP_30 nM | 1125 | 980 | 536 | 328 | ALC1 KO PARP1 KO | exp12 | uid46 |
| OP_300 nM | 1024 | 964 | 488 | 299 | ALC1 KO PARP1 KO | exp12 | uid47 |
| OP_3000 nM | 952 | 922 | 399 | 288 | ALC1 KO PARP1 KO | exp12 | uid48 |
library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "MMS", value.name = "Counts")
dataset$genotype <- relevel(dataset$genotype, ref = "WT")
dataset$Experiment <- relevel(dataset$Experiment, ref = "exp1")
dataset$UID <- relevel(dataset$UID, ref = "uid1")
dataset$Olaparib <- log10(as.numeric(gsub("OP_| nM","",gsub("NT","1",dataset$Treatment))))
dataset$MMS <- as.numeric(gsub(",",".",gsub("MMS_","",gsub("NT","0",dataset$MMS))))
dataset$Offset <- NA
for(eidx in levels(dataset$Experiment)){
dataset$Offset[dataset$Experiment == eidx] <- mean(dataset$Counts[dataset$Experiment == eidx])
}
dataset$NormCounts <- dataset$Counts / dataset$Offset
dataset$Offset2 <- NA
for(gidx in levels(dataset$genotype)){
dataset$Offset2[dataset$genotype == gidx] <- mean(dataset$NormCounts[dataset$genotype == gidx & dataset$MMS == 0 & dataset$Olaparib == 0])
}
dataset$NormCounts2 <- dataset$NormCounts / dataset$Offset2
# long format
kable(dataset, row.names = F)
| Treatment | genotype | Experiment | UID | MMS | Counts | Olaparib | Offset | NormCounts | Offset2 | NormCounts2 |
|---|---|---|---|---|---|---|---|---|---|---|
| NT | WT | exp1 | uid1 | 0e+00 | 2272 | 0.000000 | 1476.7500 | 1.5385136 | 1.532591 | 1.0038643 |
| OP_30 nM | WT | exp1 | uid2 | 0e+00 | 2050 | 1.477121 | 1476.7500 | 1.3881835 | 1.532591 | 0.9057755 |
| OP_300 nM | WT | exp1 | uid3 | 0e+00 | 1876 | 2.477121 | 1476.7500 | 1.2703572 | 1.532591 | 0.8288950 |
| OP_3000 nM | WT | exp1 | uid4 | 0e+00 | 1348 | 3.477121 | 1476.7500 | 0.9128153 | 1.532591 | 0.5956026 |
| NT | WT | exp2 | uid5 | 0e+00 | 1224 | 0.000000 | 827.6875 | 1.4788190 | 1.532591 | 0.9649142 |
| OP_30 nM | WT | exp2 | uid6 | 0e+00 | 1140 | 1.477121 | 827.6875 | 1.3773314 | 1.532591 | 0.8986946 |
| OP_300 nM | WT | exp2 | uid7 | 0e+00 | 1080 | 2.477121 | 827.6875 | 1.3048403 | 1.532591 | 0.8513949 |
| OP_3000 nM | WT | exp2 | uid8 | 0e+00 | 952 | 3.477121 | 827.6875 | 1.1501926 | 1.532591 | 0.7504888 |
| NT | WT | exp3 | uid9 | 0e+00 | 1608 | 0.000000 | 1017.4375 | 1.5804411 | 1.532591 | 1.0312215 |
| OP_30 nM | WT | exp3 | uid10 | 0e+00 | 1542 | 1.477121 | 1017.4375 | 1.5155722 | 1.532591 | 0.9888953 |
| OP_300 nM | WT | exp3 | uid11 | 0e+00 | 1208 | 2.477121 | 1017.4375 | 1.1872965 | 1.532591 | 0.7746988 |
| OP_3000 nM | WT | exp3 | uid12 | 0e+00 | 850 | 3.477121 | 1017.4375 | 0.8354322 | 1.532591 | 0.5451109 |
| NT | PARP1 KO | exp4 | uid13 | 0e+00 | 1004 | 0.000000 | 606.6250 | 1.6550587 | 1.722027 | 0.9611109 |
| OP_30 nM | PARP1 KO | exp4 | uid14 | 0e+00 | 960 | 1.477121 | 606.6250 | 1.5825263 | 1.722027 | 0.9189905 |
| OP_300 nM | PARP1 KO | exp4 | uid15 | 0e+00 | 896 | 2.477121 | 606.6250 | 1.4770245 | 1.722027 | 0.8577244 |
| OP_3000 nM | PARP1 KO | exp4 | uid16 | 0e+00 | 728 | 3.477121 | 606.6250 | 1.2000824 | 1.722027 | 0.6969011 |
| NT | PARP1 KO | exp5 | uid17 | 0e+00 | 1150 | 0.000000 | 659.5625 | 1.7435800 | 1.722027 | 1.0125162 |
| OP_30 nM | PARP1 KO | exp5 | uid18 | 0e+00 | 1030 | 1.477121 | 659.5625 | 1.5616412 | 1.722027 | 0.9068623 |
| OP_300 nM | PARP1 KO | exp5 | uid19 | 0e+00 | 900 | 2.477121 | 659.5625 | 1.3645409 | 1.722027 | 0.7924039 |
| OP_3000 nM | PARP1 KO | exp5 | uid20 | 0e+00 | 754 | 3.477121 | 659.5625 | 1.1431820 | 1.722027 | 0.6638584 |
| NT | PARP1 KO | exp6 | uid21 | 0e+00 | 988 | 0.000000 | 559.0000 | 1.7674419 | 1.722027 | 1.0263730 |
| OP_30 nM | PARP1 KO | exp6 | uid22 | 0e+00 | 1020 | 1.477121 | 559.0000 | 1.8246869 | 1.722027 | 1.0596158 |
| OP_300 nM | PARP1 KO | exp6 | uid23 | 0e+00 | 828 | 2.477121 | 559.0000 | 1.4812165 | 1.722027 | 0.8601587 |
| OP_3000 nM | PARP1 KO | exp6 | uid24 | 0e+00 | 668 | 3.477121 | 559.0000 | 1.1949911 | 1.722027 | 0.6939445 |
| NT | ALC1 KO | exp7 | uid25 | 0e+00 | 2336 | 0.000000 | 793.8750 | 2.9425287 | 3.042807 | 0.9670442 |
| OP_30 nM | ALC1 KO | exp7 | uid26 | 0e+00 | 1944 | 1.477121 | 793.8750 | 2.4487482 | 3.042807 | 0.8047662 |
| OP_300 nM | ALC1 KO | exp7 | uid27 | 0e+00 | 776 | 2.477121 | 793.8750 | 0.9774839 | 3.042807 | 0.3212441 |
| OP_3000 nM | ALC1 KO | exp7 | uid28 | 0e+00 | 405 | 3.477121 | 793.8750 | 0.5101559 | 3.042807 | 0.1676596 |
| NT | ALC1 KO | exp8 | uid29 | 0e+00 | 1760 | 0.000000 | 561.1875 | 3.1362067 | 3.042807 | 1.0306953 |
| OP_30 nM | ALC1 KO | exp8 | uid30 | 0e+00 | 952 | 1.477121 | 561.1875 | 1.6964027 | 3.042807 | 0.5575124 |
| OP_300 nM | ALC1 KO | exp8 | uid31 | 0e+00 | 422 | 2.477121 | 561.1875 | 0.7519768 | 3.042807 | 0.2471326 |
| OP_3000 nM | ALC1 KO | exp8 | uid32 | 0e+00 | 332 | 3.477121 | 561.1875 | 0.5916026 | 3.042807 | 0.1944266 |
| NT | ALC1 KO | exp9 | uid33 | 0e+00 | 1968 | 0.000000 | 645.3125 | 3.0496852 | 3.042807 | 1.0022605 |
| OP_30 nM | ALC1 KO | exp9 | uid34 | 0e+00 | 911 | 1.477121 | 645.3125 | 1.4117191 | 3.042807 | 0.4639529 |
| OP_300 nM | ALC1 KO | exp9 | uid35 | 0e+00 | 711 | 2.477121 | 645.3125 | 1.1017918 | 3.042807 | 0.3620972 |
| OP_3000 nM | ALC1 KO | exp9 | uid36 | 0e+00 | 321 | 3.477121 | 645.3125 | 0.4974334 | 3.042807 | 0.1634785 |
| NT | ALC1 KO PARP1 KO | exp10 | uid37 | 0e+00 | 2424 | 0.000000 | 1373.9375 | 1.7642724 | 1.807476 | 0.9760975 |
| OP_30 nM | ALC1 KO PARP1 KO | exp10 | uid38 | 0e+00 | 2400 | 1.477121 | 1373.9375 | 1.7468043 | 1.807476 | 0.9664332 |
| OP_300 nM | ALC1 KO PARP1 KO | exp10 | uid39 | 0e+00 | 2322 | 2.477121 | 1373.9375 | 1.6900332 | 1.807476 | 0.9350241 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp10 | uid40 | 0e+00 | 2188 | 3.477121 | 1373.9375 | 1.5925033 | 1.807476 | 0.8810649 |
| NT | ALC1 KO PARP1 KO | exp11 | uid41 | 0e+00 | 1677 | 0.000000 | 912.3125 | 1.8381859 | 1.807476 | 1.0169908 |
| OP_30 nM | ALC1 KO PARP1 KO | exp11 | uid42 | 0e+00 | 1512 | 1.477121 | 912.3125 | 1.6573268 | 1.807476 | 0.9169291 |
| OP_300 nM | ALC1 KO PARP1 KO | exp11 | uid43 | 0e+00 | 1155 | 2.477121 | 912.3125 | 1.2660136 | 1.807476 | 0.7004319 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp11 | uid44 | 0e+00 | 922 | 3.477121 | 912.3125 | 1.0106186 | 1.807476 | 0.5591327 |
| NT | ALC1 KO PARP1 KO | exp12 | uid45 | 0e+00 | 1366 | 0.000000 | 750.5625 | 1.8199684 | 1.807476 | 1.0069117 |
| OP_30 nM | ALC1 KO PARP1 KO | exp12 | uid46 | 0e+00 | 1125 | 1.477121 | 750.5625 | 1.4988758 | 1.807476 | 0.8292648 |
| OP_300 nM | ALC1 KO PARP1 KO | exp12 | uid47 | 0e+00 | 1024 | 2.477121 | 750.5625 | 1.3643101 | 1.807476 | 0.7548152 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp12 | uid48 | 0e+00 | 952 | 3.477121 | 750.5625 | 1.2683820 | 1.807476 | 0.7017423 |
| NT | WT | exp1 | uid1 | 3e-04 | 1946 | 0.000000 | 1476.7500 | 1.3177586 | 1.532591 | 0.8598239 |
| OP_30 nM | WT | exp1 | uid2 | 3e-04 | 1777 | 1.477121 | 1476.7500 | 1.2033181 | 1.532591 | 0.7851527 |
| OP_300 nM | WT | exp1 | uid3 | 3e-04 | 1568 | 2.477121 | 1476.7500 | 1.0617911 | 1.532591 | 0.6928078 |
| OP_3000 nM | WT | exp1 | uid4 | 3e-04 | 1221 | 3.477121 | 1476.7500 | 0.8268156 | 1.532591 | 0.5394887 |
| NT | WT | exp2 | uid5 | 3e-04 | 1110 | 0.000000 | 827.6875 | 1.3410859 | 1.532591 | 0.8750447 |
| OP_30 nM | WT | exp2 | uid6 | 3e-04 | 1020 | 1.477121 | 827.6875 | 1.2323492 | 1.532591 | 0.8040951 |
| OP_300 nM | WT | exp2 | uid7 | 3e-04 | 808 | 2.477121 | 827.6875 | 0.9762138 | 1.532591 | 0.6369695 |
| OP_3000 nM | WT | exp2 | uid8 | 3e-04 | 652 | 3.477121 | 827.6875 | 0.7877369 | 1.532591 | 0.5139902 |
| NT | WT | exp3 | uid9 | 3e-04 | 1553 | 0.000000 | 1017.4375 | 1.5263837 | 1.532591 | 0.9959496 |
| OP_30 nM | WT | exp3 | uid10 | 3e-04 | 1264 | 1.477121 | 1017.4375 | 1.2423368 | 1.532591 | 0.8106119 |
| OP_300 nM | WT | exp3 | uid11 | 3e-04 | 920 | 2.477121 | 1017.4375 | 0.9042324 | 1.532591 | 0.5900024 |
| OP_3000 nM | WT | exp3 | uid12 | 3e-04 | 732 | 3.477121 | 1017.4375 | 0.7194545 | 1.532591 | 0.4694367 |
| NT | PARP1 KO | exp4 | uid13 | 3e-04 | 756 | 0.000000 | 606.6250 | 1.2462394 | 1.722027 | 0.7237050 |
| OP_30 nM | PARP1 KO | exp4 | uid14 | 3e-04 | 740 | 1.477121 | 606.6250 | 1.2198640 | 1.722027 | 0.7083885 |
| OP_300 nM | PARP1 KO | exp4 | uid15 | 3e-04 | 670 | 2.477121 | 606.6250 | 1.1044715 | 1.722027 | 0.6413788 |
| OP_3000 nM | PARP1 KO | exp4 | uid16 | 3e-04 | 630 | 3.477121 | 606.6250 | 1.0385329 | 1.722027 | 0.6030875 |
| NT | PARP1 KO | exp5 | uid17 | 3e-04 | 955 | 0.000000 | 659.5625 | 1.4479295 | 1.722027 | 0.8408286 |
| OP_30 nM | PARP1 KO | exp5 | uid18 | 3e-04 | 888 | 1.477121 | 659.5625 | 1.3463470 | 1.722027 | 0.7818386 |
| OP_300 nM | PARP1 KO | exp5 | uid19 | 3e-04 | 857 | 2.477121 | 659.5625 | 1.2993462 | 1.722027 | 0.7545446 |
| OP_3000 nM | PARP1 KO | exp5 | uid20 | 3e-04 | 688 | 3.477121 | 659.5625 | 1.0431157 | 1.722027 | 0.6057488 |
| NT | PARP1 KO | exp6 | uid21 | 3e-04 | 756 | 0.000000 | 559.0000 | 1.3524150 | 1.722027 | 0.7853623 |
| OP_30 nM | PARP1 KO | exp6 | uid22 | 3e-04 | 678 | 1.477121 | 559.0000 | 1.2128801 | 1.722027 | 0.7043329 |
| OP_300 nM | PARP1 KO | exp6 | uid23 | 3e-04 | 655 | 2.477121 | 559.0000 | 1.1717352 | 1.722027 | 0.6804396 |
| OP_3000 nM | PARP1 KO | exp6 | uid24 | 3e-04 | 545 | 3.477121 | 559.0000 | 0.9749553 | 1.722027 | 0.5661673 |
| NT | ALC1 KO | exp7 | uid25 | 3e-04 | 1756 | 0.000000 | 793.8750 | 2.2119351 | 3.042807 | 0.7269390 |
| OP_30 nM | ALC1 KO | exp7 | uid26 | 3e-04 | 896 | 1.477121 | 793.8750 | 1.1286412 | 3.042807 | 0.3709211 |
| OP_300 nM | ALC1 KO | exp7 | uid27 | 3e-04 | 616 | 2.477121 | 793.8750 | 0.7759408 | 3.042807 | 0.2550082 |
| OP_3000 nM | ALC1 KO | exp7 | uid28 | 3e-04 | 324 | 3.477121 | 793.8750 | 0.4081247 | 3.042807 | 0.1341277 |
| NT | ALC1 KO | exp8 | uid29 | 3e-04 | 1540 | 0.000000 | 561.1875 | 2.7441809 | 3.042807 | 0.9018584 |
| OP_30 nM | ALC1 KO | exp8 | uid30 | 3e-04 | 710 | 1.477121 | 561.1875 | 1.2651743 | 3.042807 | 0.4157918 |
| OP_300 nM | ALC1 KO | exp8 | uid31 | 3e-04 | 368 | 2.477121 | 561.1875 | 0.6557523 | 3.042807 | 0.2155090 |
| OP_3000 nM | ALC1 KO | exp8 | uid32 | 3e-04 | 210 | 3.477121 | 561.1875 | 0.3742065 | 3.042807 | 0.1229807 |
| NT | ALC1 KO | exp9 | uid33 | 3e-04 | 1568 | 0.000000 | 645.3125 | 2.4298305 | 3.042807 | 0.7985490 |
| OP_30 nM | ALC1 KO | exp9 | uid34 | 3e-04 | 896 | 1.477121 | 645.3125 | 1.3884746 | 3.042807 | 0.4563137 |
| OP_300 nM | ALC1 KO | exp9 | uid35 | 3e-04 | 669 | 2.477121 | 645.3125 | 1.0367070 | 3.042807 | 0.3407075 |
| OP_3000 nM | ALC1 KO | exp9 | uid36 | 3e-04 | 114 | 3.477121 | 645.3125 | 0.1766586 | 3.042807 | 0.0580578 |
| NT | ALC1 KO PARP1 KO | exp10 | uid37 | 3e-04 | 1855 | 0.000000 | 1373.9375 | 1.3501342 | 1.807476 | 0.7469723 |
| OP_30 nM | ALC1 KO PARP1 KO | exp10 | uid38 | 3e-04 | 1755 | 1.477121 | 1373.9375 | 1.2773507 | 1.807476 | 0.7067043 |
| OP_300 nM | ALC1 KO PARP1 KO | exp10 | uid39 | 3e-04 | 1641 | 2.477121 | 1373.9375 | 1.1943775 | 1.807476 | 0.6607987 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp10 | uid40 | 3e-04 | 1555 | 3.477121 | 1373.9375 | 1.1317837 | 1.807476 | 0.6261682 |
| NT | ALC1 KO PARP1 KO | exp11 | uid41 | 3e-04 | 1489 | 0.000000 | 912.3125 | 1.6321162 | 1.807476 | 0.9029811 |
| OP_30 nM | ALC1 KO PARP1 KO | exp11 | uid42 | 3e-04 | 1255 | 1.477121 | 912.3125 | 1.3756251 | 1.807476 | 0.7610754 |
| OP_300 nM | ALC1 KO PARP1 KO | exp11 | uid43 | 3e-04 | 1099 | 2.477121 | 912.3125 | 1.2046311 | 1.807476 | 0.6664716 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp11 | uid44 | 3e-04 | 958 | 3.477121 | 912.3125 | 1.0500788 | 1.807476 | 0.5809643 |
| NT | ALC1 KO PARP1 KO | exp12 | uid45 | 3e-04 | 1120 | 0.000000 | 750.5625 | 1.4922142 | 1.807476 | 0.8255792 |
| OP_30 nM | ALC1 KO PARP1 KO | exp12 | uid46 | 3e-04 | 980 | 1.477121 | 750.5625 | 1.3056874 | 1.807476 | 0.7223818 |
| OP_300 nM | ALC1 KO PARP1 KO | exp12 | uid47 | 3e-04 | 964 | 2.477121 | 750.5625 | 1.2843701 | 1.807476 | 0.7105878 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp12 | uid48 | 3e-04 | 922 | 3.477121 | 750.5625 | 1.2284120 | 1.807476 | 0.6796286 |
| NT | WT | exp1 | uid1 | 6e-04 | 1676 | 0.000000 | 1476.7500 | 1.1349247 | 1.532591 | 0.7405267 |
| OP_30 nM | WT | exp1 | uid2 | 6e-04 | 1512 | 1.477121 | 1476.7500 | 1.0238700 | 1.532591 | 0.6680646 |
| OP_300 nM | WT | exp1 | uid3 | 6e-04 | 1232 | 2.477121 | 1476.7500 | 0.8342644 | 1.532591 | 0.5443490 |
| OP_3000 nM | WT | exp1 | uid4 | 6e-04 | 888 | 3.477121 | 1476.7500 | 0.6013205 | 1.532591 | 0.3923554 |
| NT | WT | exp2 | uid5 | 6e-04 | 1051 | 0.000000 | 827.6875 | 1.2698029 | 1.532591 | 0.8285333 |
| OP_30 nM | WT | exp2 | uid6 | 6e-04 | 892 | 1.477121 | 827.6875 | 1.0777014 | 1.532591 | 0.7031891 |
| OP_300 nM | WT | exp2 | uid7 | 6e-04 | 596 | 2.477121 | 827.6875 | 0.7200785 | 1.532591 | 0.4698438 |
| OP_3000 nM | WT | exp2 | uid8 | 6e-04 | 432 | 3.477121 | 827.6875 | 0.5219361 | 1.532591 | 0.3405579 |
| NT | WT | exp3 | uid9 | 6e-04 | 1295 | 0.000000 | 1017.4375 | 1.2728055 | 1.532591 | 0.8304925 |
| OP_30 nM | WT | exp3 | uid10 | 6e-04 | 1120 | 1.477121 | 1017.4375 | 1.1008047 | 1.532591 | 0.7182637 |
| OP_300 nM | WT | exp3 | uid11 | 6e-04 | 755 | 2.477121 | 1017.4375 | 0.7420603 | 1.532591 | 0.4841867 |
| OP_3000 nM | WT | exp3 | uid12 | 6e-04 | 600 | 3.477121 | 1017.4375 | 0.5897168 | 1.532591 | 0.3847841 |
| NT | PARP1 KO | exp4 | uid13 | 6e-04 | 615 | 0.000000 | 606.6250 | 1.0138059 | 1.722027 | 0.5887283 |
| OP_30 nM | PARP1 KO | exp4 | uid14 | 6e-04 | 540 | 1.477121 | 606.6250 | 0.8901710 | 1.722027 | 0.5169321 |
| OP_300 nM | PARP1 KO | exp4 | uid15 | 6e-04 | 522 | 2.477121 | 606.6250 | 0.8604987 | 1.722027 | 0.4997011 |
| OP_3000 nM | PARP1 KO | exp4 | uid16 | 6e-04 | 477 | 3.477121 | 606.6250 | 0.7863177 | 1.722027 | 0.4566234 |
| NT | PARP1 KO | exp5 | uid17 | 6e-04 | 677 | 0.000000 | 659.5625 | 1.0264380 | 1.722027 | 0.5960639 |
| OP_30 nM | PARP1 KO | exp5 | uid18 | 6e-04 | 544 | 1.477121 | 659.5625 | 0.8247892 | 1.722027 | 0.4789642 |
| OP_300 nM | PARP1 KO | exp5 | uid19 | 6e-04 | 512 | 2.477121 | 659.5625 | 0.7762722 | 1.722027 | 0.4507898 |
| OP_3000 nM | PARP1 KO | exp5 | uid20 | 6e-04 | 410 | 3.477121 | 659.5625 | 0.6216242 | 1.722027 | 0.3609840 |
| NT | PARP1 KO | exp6 | uid21 | 6e-04 | 621 | 0.000000 | 559.0000 | 1.1109123 | 1.722027 | 0.6451191 |
| OP_30 nM | PARP1 KO | exp6 | uid22 | 6e-04 | 554 | 1.477121 | 559.0000 | 0.9910555 | 1.722027 | 0.5755168 |
| OP_300 nM | PARP1 KO | exp6 | uid23 | 6e-04 | 468 | 2.477121 | 559.0000 | 0.8372093 | 1.722027 | 0.4861767 |
| OP_3000 nM | PARP1 KO | exp6 | uid24 | 6e-04 | 365 | 3.477121 | 559.0000 | 0.6529517 | 1.722027 | 0.3791763 |
| NT | ALC1 KO | exp7 | uid25 | 6e-04 | 1396 | 0.000000 | 793.8750 | 1.7584632 | 3.042807 | 0.5779083 |
| OP_30 nM | ALC1 KO | exp7 | uid26 | 6e-04 | 740 | 1.477121 | 793.8750 | 0.9321367 | 3.042807 | 0.3063411 |
| OP_300 nM | ALC1 KO | exp7 | uid27 | 6e-04 | 441 | 2.477121 | 793.8750 | 0.5555031 | 3.042807 | 0.1825627 |
| OP_3000 nM | ALC1 KO | exp7 | uid28 | 6e-04 | 112 | 3.477121 | 793.8750 | 0.1410801 | 3.042807 | 0.0463651 |
| NT | ALC1 KO | exp8 | uid29 | 6e-04 | 966 | 0.000000 | 561.1875 | 1.7213498 | 3.042807 | 0.5657112 |
| OP_30 nM | ALC1 KO | exp8 | uid30 | 6e-04 | 450 | 1.477121 | 561.1875 | 0.8018710 | 3.042807 | 0.2635300 |
| OP_300 nM | ALC1 KO | exp8 | uid31 | 6e-04 | 320 | 2.477121 | 561.1875 | 0.5702194 | 3.042807 | 0.1873991 |
| OP_3000 nM | ALC1 KO | exp8 | uid32 | 6e-04 | 102 | 3.477121 | 561.1875 | 0.1817574 | 3.042807 | 0.0597335 |
| NT | ALC1 KO | exp9 | uid33 | 6e-04 | 999 | 0.000000 | 645.3125 | 1.5480872 | 3.042807 | 0.5087694 |
| OP_30 nM | ALC1 KO | exp9 | uid34 | 6e-04 | 620 | 1.477121 | 645.3125 | 0.9607748 | 3.042807 | 0.3157528 |
| OP_300 nM | ALC1 KO | exp9 | uid35 | 6e-04 | 410 | 2.477121 | 645.3125 | 0.6353511 | 3.042807 | 0.2088043 |
| OP_3000 nM | ALC1 KO | exp9 | uid36 | 6e-04 | 95 | 3.477121 | 645.3125 | 0.1472155 | 3.042807 | 0.0483815 |
| NT | ALC1 KO PARP1 KO | exp10 | uid37 | 6e-04 | 1355 | 0.000000 | 1373.9375 | 0.9862166 | 1.807476 | 0.5456321 |
| OP_30 nM | ALC1 KO PARP1 KO | exp10 | uid38 | 6e-04 | 1080 | 1.477121 | 1373.9375 | 0.7860620 | 1.807476 | 0.4348949 |
| OP_300 nM | ALC1 KO PARP1 KO | exp10 | uid39 | 6e-04 | 850 | 2.477121 | 1373.9375 | 0.6186599 | 1.807476 | 0.3422784 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp10 | uid40 | 6e-04 | 742 | 3.477121 | 1373.9375 | 0.5400537 | 1.807476 | 0.2987889 |
| NT | ALC1 KO PARP1 KO | exp11 | uid41 | 6e-04 | 1023 | 0.000000 | 912.3125 | 1.1213263 | 1.807476 | 0.6203826 |
| OP_30 nM | ALC1 KO PARP1 KO | exp11 | uid42 | 6e-04 | 741 | 1.477121 | 912.3125 | 0.8122217 | 1.807476 | 0.4493680 |
| OP_300 nM | ALC1 KO PARP1 KO | exp11 | uid43 | 6e-04 | 612 | 2.477121 | 912.3125 | 0.6708228 | 1.807476 | 0.3711379 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp11 | uid44 | 6e-04 | 588 | 3.477121 | 912.3125 | 0.6445160 | 1.807476 | 0.3565835 |
| NT | ALC1 KO PARP1 KO | exp12 | uid45 | 6e-04 | 785 | 0.000000 | 750.5625 | 1.0458823 | 1.807476 | 0.5786425 |
| OP_30 nM | ALC1 KO PARP1 KO | exp12 | uid46 | 6e-04 | 536 | 1.477121 | 750.5625 | 0.7141311 | 1.807476 | 0.3950986 |
| OP_300 nM | ALC1 KO PARP1 KO | exp12 | uid47 | 6e-04 | 488 | 2.477121 | 750.5625 | 0.6501790 | 1.807476 | 0.3597166 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp12 | uid48 | 6e-04 | 399 | 3.477121 | 750.5625 | 0.5316013 | 1.807476 | 0.2941126 |
| NT | WT | exp1 | uid1 | 1e-03 | 1422 | 0.000000 | 1476.7500 | 0.9629253 | 1.532591 | 0.6282989 |
| OP_30 nM | WT | exp1 | uid2 | 1e-03 | 1228 | 1.477121 | 1476.7500 | 0.8315558 | 1.532591 | 0.5425816 |
| OP_300 nM | WT | exp1 | uid3 | 1e-03 | 1044 | 2.477121 | 1476.7500 | 0.7069578 | 1.532591 | 0.4612827 |
| OP_3000 nM | WT | exp1 | uid4 | 1e-03 | 568 | 3.477121 | 1476.7500 | 0.3846284 | 1.532591 | 0.2509661 |
| NT | WT | exp2 | uid5 | 1e-03 | 920 | 0.000000 | 827.6875 | 1.1115306 | 1.532591 | 0.7252623 |
| OP_30 nM | WT | exp2 | uid6 | 1e-03 | 696 | 1.477121 | 827.6875 | 0.8408971 | 1.532591 | 0.5486767 |
| OP_300 nM | WT | exp2 | uid7 | 1e-03 | 425 | 2.477121 | 827.6875 | 0.5134788 | 1.532591 | 0.3350396 |
| OP_3000 nM | WT | exp2 | uid8 | 1e-03 | 245 | 3.477121 | 827.6875 | 0.2960054 | 1.532591 | 0.1931405 |
| NT | WT | exp3 | uid9 | 1e-03 | 1121 | 0.000000 | 1017.4375 | 1.1017876 | 1.532591 | 0.7189051 |
| OP_30 nM | WT | exp3 | uid10 | 1e-03 | 838 | 1.477121 | 1017.4375 | 0.8236378 | 1.532591 | 0.5374152 |
| OP_300 nM | WT | exp3 | uid11 | 1e-03 | 488 | 2.477121 | 1017.4375 | 0.4796363 | 1.532591 | 0.3129578 |
| OP_3000 nM | WT | exp3 | uid12 | 1e-03 | 385 | 3.477121 | 1017.4375 | 0.3784016 | 1.532591 | 0.2469032 |
| NT | PARP1 KO | exp4 | uid13 | 1e-03 | 388 | 0.000000 | 606.6250 | 0.6396044 | 1.722027 | 0.3714253 |
| OP_30 nM | PARP1 KO | exp4 | uid14 | 1e-03 | 324 | 1.477121 | 606.6250 | 0.5341026 | 1.722027 | 0.3101593 |
| OP_300 nM | PARP1 KO | exp4 | uid15 | 1e-03 | 244 | 2.477121 | 606.6250 | 0.4022254 | 1.722027 | 0.2335767 |
| OP_3000 nM | PARP1 KO | exp4 | uid16 | 1e-03 | 212 | 3.477121 | 606.6250 | 0.3494746 | 1.722027 | 0.2029437 |
| NT | PARP1 KO | exp5 | uid17 | 1e-03 | 422 | 0.000000 | 659.5625 | 0.6398181 | 1.722027 | 0.3715494 |
| OP_30 nM | PARP1 KO | exp5 | uid18 | 1e-03 | 398 | 1.477121 | 659.5625 | 0.6034303 | 1.722027 | 0.3504186 |
| OP_300 nM | PARP1 KO | exp5 | uid19 | 1e-03 | 212 | 2.477121 | 659.5625 | 0.3214252 | 1.722027 | 0.1866552 |
| OP_3000 nM | PARP1 KO | exp5 | uid20 | 1e-03 | 156 | 3.477121 | 659.5625 | 0.2365204 | 1.722027 | 0.1373500 |
| NT | PARP1 KO | exp6 | uid21 | 1e-03 | 328 | 0.000000 | 559.0000 | 0.5867621 | 1.722027 | 0.3407392 |
| OP_30 nM | PARP1 KO | exp6 | uid22 | 1e-03 | 222 | 1.477121 | 559.0000 | 0.3971377 | 1.722027 | 0.2306223 |
| OP_300 nM | PARP1 KO | exp6 | uid23 | 1e-03 | 128 | 2.477121 | 559.0000 | 0.2289803 | 1.722027 | 0.1329714 |
| OP_3000 nM | PARP1 KO | exp6 | uid24 | 1e-03 | 120 | 3.477121 | 559.0000 | 0.2146691 | 1.722027 | 0.1246607 |
| NT | ALC1 KO | exp7 | uid25 | 1e-03 | 700 | 0.000000 | 793.8750 | 0.8817509 | 3.042807 | 0.2897821 |
| OP_30 nM | ALC1 KO | exp7 | uid26 | 1e-03 | 121 | 1.477121 | 793.8750 | 0.1524169 | 3.042807 | 0.0500909 |
| OP_300 nM | ALC1 KO | exp7 | uid27 | 1e-03 | 101 | 2.477121 | 793.8750 | 0.1272241 | 3.042807 | 0.0418114 |
| OP_3000 nM | ALC1 KO | exp7 | uid28 | 1e-03 | 38 | 3.477121 | 793.8750 | 0.0478665 | 3.042807 | 0.0157310 |
| NT | ALC1 KO | exp8 | uid29 | 1e-03 | 588 | 0.000000 | 561.1875 | 1.0477781 | 3.042807 | 0.3443459 |
| OP_30 nM | ALC1 KO | exp8 | uid30 | 1e-03 | 120 | 1.477121 | 561.1875 | 0.2138323 | 3.042807 | 0.0702747 |
| OP_300 nM | ALC1 KO | exp8 | uid31 | 1e-03 | 77 | 2.477121 | 561.1875 | 0.1372090 | 3.042807 | 0.0450929 |
| OP_3000 nM | ALC1 KO | exp8 | uid32 | 1e-03 | 62 | 3.477121 | 561.1875 | 0.1104800 | 3.042807 | 0.0363086 |
| NT | ALC1 KO | exp9 | uid33 | 1e-03 | 652 | 0.000000 | 645.3125 | 1.0103632 | 3.042807 | 0.3320497 |
| OP_30 nM | ALC1 KO | exp9 | uid34 | 1e-03 | 210 | 1.477121 | 645.3125 | 0.3254237 | 3.042807 | 0.1069485 |
| OP_300 nM | ALC1 KO | exp9 | uid35 | 1e-03 | 141 | 2.477121 | 645.3125 | 0.2184988 | 3.042807 | 0.0718083 |
| OP_3000 nM | ALC1 KO | exp9 | uid36 | 1e-03 | 40 | 3.477121 | 645.3125 | 0.0619855 | 3.042807 | 0.0203711 |
| NT | ALC1 KO PARP1 KO | exp10 | uid37 | 1e-03 | 751 | 0.000000 | 1373.9375 | 0.5466042 | 1.807476 | 0.3024130 |
| OP_30 nM | ALC1 KO PARP1 KO | exp10 | uid38 | 1e-03 | 422 | 1.477121 | 1373.9375 | 0.3071464 | 1.807476 | 0.1699312 |
| OP_300 nM | ALC1 KO PARP1 KO | exp10 | uid39 | 1e-03 | 355 | 2.477121 | 1373.9375 | 0.2583815 | 1.807476 | 0.1429516 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp10 | uid40 | 1e-03 | 288 | 3.477121 | 1373.9375 | 0.2096165 | 1.807476 | 0.1159720 |
| NT | ALC1 KO PARP1 KO | exp11 | uid41 | 1e-03 | 612 | 0.000000 | 912.3125 | 0.6708228 | 1.807476 | 0.3711379 |
| OP_30 nM | ALC1 KO PARP1 KO | exp11 | uid42 | 1e-03 | 355 | 1.477121 | 912.3125 | 0.3891211 | 1.807476 | 0.2152843 |
| OP_300 nM | ALC1 KO PARP1 KO | exp11 | uid43 | 1e-03 | 355 | 2.477121 | 912.3125 | 0.3891211 | 1.807476 | 0.2152843 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp11 | uid44 | 1e-03 | 244 | 3.477121 | 912.3125 | 0.2674522 | 1.807476 | 0.1479700 |
| NT | ALC1 KO PARP1 KO | exp12 | uid45 | 1e-03 | 433 | 0.000000 | 750.5625 | 0.5769007 | 1.807476 | 0.3191748 |
| OP_30 nM | ALC1 KO PARP1 KO | exp12 | uid46 | 1e-03 | 328 | 1.477121 | 750.5625 | 0.4370056 | 1.807476 | 0.2417768 |
| OP_300 nM | ALC1 KO PARP1 KO | exp12 | uid47 | 1e-03 | 299 | 2.477121 | 750.5625 | 0.3983679 | 1.807476 | 0.2204002 |
| OP_3000 nM | ALC1 KO PARP1 KO | exp12 | uid48 | 1e-03 | 288 | 3.477121 | 750.5625 | 0.3837122 | 1.807476 | 0.2122918 |
library(ggplot2)
# raw data
ggplot(dataset, aes(x=MMS, y=Counts, color=Treatment)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
geom_point(aes(colour=Treatment, shape=Experiment), size=2) +
facet_grid(. ~ genotype) +
xlab(label = "MMS (%)") +
scale_shape_manual(values=1:19) +
scale_color_manual(values=c('#000000','#EE0000','#0000EE','#888888'))
# NormCounts Linear
ggplot(dataset, aes(x=MMS, y=NormCounts, color=Treatment)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
geom_point(aes(colour=Treatment), size=2) +
facet_grid(. ~ genotype) +
xlab(label = "MMS (%)") +
scale_color_manual(values=c('#000000','#EE0000','#0000EE','#888888'))
# NormCounts2 Linear
ggplot(dataset, aes(x=MMS, y=NormCounts2, color=Treatment)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
geom_point(aes(colour=Treatment), size=2) +
facet_grid(. ~ genotype) +
xlab(label = "MMS (%)") +
scale_color_manual(values=c('#000000','#EE0000','#0000EE','#888888'))
# NormCounts Quadratic
ggplot(dataset, aes(x=MMS, y=NormCounts, color=Treatment)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
geom_point(aes(colour=Treatment), size=2) +
facet_grid(. ~ genotype) +
xlab(label = "MMS (%)") +
scale_color_manual(values=c('#000000','#EE0000','#0000EE','#888888'))
# NormCounts2 Quadratic
ggplot(dataset, aes(x=MMS, y=NormCounts2, color=Treatment)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
geom_point(aes(colour=Treatment), size=2) +
facet_grid(. ~ genotype) +
xlab(label = "MMS (%)") +
scale_color_manual(values=c('#000000','#EE0000','#0000EE','#888888'))
# NormCounts Cubic
ggplot(dataset, aes(x=MMS, y=NormCounts, color=Treatment)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
geom_point(aes(colour=Treatment), size=2) +
facet_grid(. ~ genotype) +
xlab(label = "MMS (%)") +
scale_color_manual(values=c('#000000','#EE0000','#0000EE','#888888'))
# NormCounts2 Cubic
ggplot(dataset, aes(x=MMS, y=NormCounts2, color=Treatment)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
geom_point(aes(colour=Treatment), size=2) +
facet_grid(. ~ genotype) +
xlab(label = "MMS (%)") +
scale_color_manual(values=c('#000000','#EE0000','#0000EE','#888888'))
cairo_pdf("Figure3B.pdf", width = 10, height = 4, family = "Arial")
datasubset <- dataset[dataset$genotype %in% levels(dataset$genotype),]
datasubset$genotype <- relevel(datasubset$genotype, ref = "WT")
ggplot(datasubset, aes(x=MMS, y=NormCounts2, color=Treatment)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14), axis.text.x = element_text(angle = 90, hjust = 1)) +
geom_point(aes(colour = Treatment)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=TRUE, fill='#DDDDDD', size=0.5) +
facet_grid(. ~ genotype) +
xlab(label = "MMS (%)") +
ylab(label = "Normalized Counts") +
scale_x_continuous(labels = function(x) format(x, scientific = TRUE)) +
scale_color_manual(values=c('#000000','#EE0000','#0000EE','#888888'))
dev.off()
## quartz_off_screen
## 2
library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)
fit1 <- lm(Counts ~ MMS*Olaparib*genotype, data = dataset)
print(summary(fit1))
##
## Call:
## lm(formula = Counts ~ MMS * Olaparib * genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -531.63 -140.29 -14.33 72.49 801.64
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1755.63 103.93 16.893 < 2e-16
## MMS -580310.59 172614.81 -3.362 0.000950
## Olaparib -182.98 46.01 -3.977 0.000102
## genotypeALC1 KO 225.50 146.98 1.534 0.126765
## genotypeALC1 KO PARP1 KO 90.02 146.98 0.612 0.540998
## genotypePARP1 KO -688.65 146.98 -4.685 5.57e-06
## MMS:Olaparib -37105.43 76419.41 -0.486 0.627890
## MMS:genotypeALC1 KO -863845.73 244114.21 -3.539 0.000514
## MMS:genotypeALC1 KO PARP1 KO -718146.86 244114.21 -2.942 0.003702
## MMS:genotypePARP1 KO -113007.47 244114.21 -0.463 0.643987
## Olaparib:genotypeALC1 KO -311.91 65.07 -4.794 3.47e-06
## Olaparib:genotypeALC1 KO PARP1 KO 51.66 65.07 0.794 0.428313
## Olaparib:genotypePARP1 KO 103.05 65.07 1.584 0.115059
## MMS:Olaparib:genotypeALC1 KO 366939.29 108073.37 3.395 0.000847
## MMS:Olaparib:genotypeALC1 KO PARP1 KO 68105.75 108073.37 0.630 0.529394
## MMS:Olaparib:genotypePARP1 KO 58904.08 108073.37 0.545 0.586417
##
## (Intercept) ***
## MMS ***
## Olaparib ***
## genotypeALC1 KO
## genotypeALC1 KO PARP1 KO
## genotypePARP1 KO ***
## MMS:Olaparib
## MMS:genotypeALC1 KO ***
## MMS:genotypeALC1 KO PARP1 KO **
## MMS:genotypePARP1 KO
## Olaparib:genotypeALC1 KO ***
## Olaparib:genotypeALC1 KO PARP1 KO
## Olaparib:genotypePARP1 KO
## MMS:Olaparib:genotypeALC1 KO ***
## MMS:Olaparib:genotypeALC1 KO PARP1 KO
## MMS:Olaparib:genotypePARP1 KO
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 251.7 on 176 degrees of freedom
## Multiple R-squared: 0.7979, Adjusted R-squared: 0.7807
## F-statistic: 46.32 on 15 and 176 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit1))
## AIC: 2684.934
simres <- simulateResiduals(fittedModel = fit1)
plot(simres)
fit2 <- lm(NormCounts ~ MMS*Olaparib*genotype, data = dataset)
print(summary(fit2))
##
## Call:
## lm(formula = NormCounts ~ MMS * Olaparib * genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45887 -0.07451 0.00108 0.06737 0.57816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.584e+00 5.444e-02 29.098 < 2e-16
## MMS -5.061e+02 9.042e+01 -5.597 8.25e-08
## Olaparib -1.630e-01 2.410e-02 -6.761 1.95e-10
## genotypeALC1 KO 1.388e+00 7.699e-02 18.028 < 2e-16
## genotypeALC1 KO PARP1 KO 2.477e-01 7.699e-02 3.218 0.00154
## genotypePARP1 KO 1.713e-01 7.699e-02 2.224 0.02740
## MMS:Olaparib -4.655e+01 4.003e+01 -1.163 0.24644
## MMS:genotypeALC1 KO -1.652e+03 1.279e+02 -12.916 < 2e-16
## MMS:genotypeALC1 KO PARP1 KO -7.766e+02 1.279e+02 -6.073 7.54e-09
## MMS:genotypePARP1 KO -6.373e+02 1.279e+02 -4.984 1.48e-06
## Olaparib:genotypeALC1 KO -5.829e-01 3.409e-02 -17.100 < 2e-16
## Olaparib:genotypeALC1 KO PARP1 KO 2.004e-02 3.409e-02 0.588 0.55726
## Olaparib:genotypePARP1 KO 3.157e-02 3.409e-02 0.926 0.35560
## MMS:Olaparib:genotypeALC1 KO 5.432e+02 5.661e+01 9.595 < 2e-16
## MMS:Olaparib:genotypeALC1 KO PARP1 KO 9.510e+01 5.661e+01 1.680 0.09478
## MMS:Olaparib:genotypePARP1 KO 8.249e+01 5.661e+01 1.457 0.14687
##
## (Intercept) ***
## MMS ***
## Olaparib ***
## genotypeALC1 KO ***
## genotypeALC1 KO PARP1 KO **
## genotypePARP1 KO *
## MMS:Olaparib
## MMS:genotypeALC1 KO ***
## MMS:genotypeALC1 KO PARP1 KO ***
## MMS:genotypePARP1 KO ***
## Olaparib:genotypeALC1 KO ***
## Olaparib:genotypeALC1 KO PARP1 KO
## Olaparib:genotypePARP1 KO
## MMS:Olaparib:genotypeALC1 KO ***
## MMS:Olaparib:genotypeALC1 KO PARP1 KO .
## MMS:Olaparib:genotypePARP1 KO
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1318 on 176 degrees of freedom
## Multiple R-squared: 0.9495, Adjusted R-squared: 0.9452
## F-statistic: 220.6 on 15 and 176 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit2))
## AIC: -215.9174
simres <- simulateResiduals(fittedModel = fit2)
plot(simres)
fit3 <- lm(NormCounts2 ~ MMS*Olaparib*genotype, data = dataset)
print(summary(fit3))
##
## Call:
## lm(formula = NormCounts2 ~ MMS * Olaparib * genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.179482 -0.038679 0.000384 0.036186 0.190008
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.03368 0.02504 41.285 < 2e-16
## MMS -330.20788 41.58514 -7.941 2.29e-13
## Olaparib -0.10632 0.01108 -9.592 < 2e-16
## genotypeALC1 KO -0.05686 0.03541 -1.606 0.1101
## genotypeALC1 KO PARP1 KO -0.02015 0.03541 -0.569 0.5701
## genotypePARP1 KO -0.01426 0.03541 -0.403 0.6877
## MMS:Olaparib -30.37600 18.41042 -1.650 0.1007
## MMS:genotypeALC1 KO -378.93319 58.81027 -6.443 1.08e-09
## MMS:genotypeALC1 KO PARP1 KO -379.44662 58.81027 -6.452 1.03e-09
## MMS:genotypePARP1 KO -333.75987 58.81027 -5.675 5.61e-08
## Olaparib:genotypeALC1 KO -0.13878 0.01568 -8.853 8.94e-16
## Olaparib:genotypeALC1 KO PARP1 KO 0.02726 0.01568 1.739 0.0838
## Olaparib:genotypePARP1 KO 0.03003 0.01568 1.916 0.0570
## MMS:Olaparib:genotypeALC1 KO 193.60495 26.03627 7.436 4.37e-12
## MMS:Olaparib:genotypeALC1 KO PARP1 KO 57.23286 26.03627 2.198 0.0292
## MMS:Olaparib:genotypePARP1 KO 51.24607 26.03627 1.968 0.0506
##
## (Intercept) ***
## MMS ***
## Olaparib ***
## genotypeALC1 KO
## genotypeALC1 KO PARP1 KO
## genotypePARP1 KO
## MMS:Olaparib
## MMS:genotypeALC1 KO ***
## MMS:genotypeALC1 KO PARP1 KO ***
## MMS:genotypePARP1 KO ***
## Olaparib:genotypeALC1 KO ***
## Olaparib:genotypeALC1 KO PARP1 KO .
## Olaparib:genotypePARP1 KO .
## MMS:Olaparib:genotypeALC1 KO ***
## MMS:Olaparib:genotypeALC1 KO PARP1 KO *
## MMS:Olaparib:genotypePARP1 KO .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06063 on 176 degrees of freedom
## Multiple R-squared: 0.9578, Adjusted R-squared: 0.9542
## F-statistic: 266.6 on 15 and 176 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit3))
## AIC: -514.1992
simres <- simulateResiduals(fittedModel = fit3)
plot(simres)
fit4 <- lmer(Counts ~ MMS*Olaparib*genotype + (1|UID), data = dataset)
print(summary(fit4))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ MMS * Olaparib * genotype + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 2285.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2898 -0.3944 -0.0059 0.3584 3.9273
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 45843 214.1
## Residual 21652 147.1
## Number of obs: 192, groups: UID, 48
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1755.63 124.51 54.65 14.101
## MMS -580310.59 100932.46 136.00 -5.749
## Olaparib -182.98 55.12 54.65 -3.320
## genotypeALC1 KO 225.50 176.08 54.65 1.281
## genotypeALC1 KO PARP1 KO 90.02 176.08 54.65 0.511
## genotypePARP1 KO -688.65 176.08 54.65 -3.911
## MMS:Olaparib -37105.43 44684.46 136.00 -0.830
## MMS:genotypeALC1 KO -863845.73 142740.05 136.00 -6.052
## MMS:genotypeALC1 KO PARP1 KO -718146.86 142740.05 136.00 -5.031
## MMS:genotypePARP1 KO -113007.47 142740.05 136.00 -0.792
## Olaparib:genotypeALC1 KO -311.91 77.95 54.65 -4.001
## Olaparib:genotypeALC1 KO PARP1 KO 51.66 77.95 54.65 0.663
## Olaparib:genotypePARP1 KO 103.05 77.95 54.65 1.322
## MMS:Olaparib:genotypeALC1 KO 366939.29 63193.36 136.00 5.807
## MMS:Olaparib:genotypeALC1 KO PARP1 KO 68105.75 63193.36 136.00 1.078
## MMS:Olaparib:genotypePARP1 KO 58904.08 63193.36 136.00 0.932
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## MMS 5.64e-08 ***
## Olaparib 0.001610 **
## genotypeALC1 KO 0.205725
## genotypeALC1 KO PARP1 KO 0.611228
## genotypePARP1 KO 0.000256 ***
## MMS:Olaparib 0.407775
## MMS:genotypeALC1 KO 1.31e-08 ***
## MMS:genotypeALC1 KO PARP1 KO 1.51e-06 ***
## MMS:genotypePARP1 KO 0.429914
## Olaparib:genotypeALC1 KO 0.000191 ***
## Olaparib:genotypeALC1 KO PARP1 KO 0.510314
## Olaparib:genotypePARP1 KO 0.191699
## MMS:Olaparib:genotypeALC1 KO 4.29e-08 ***
## MMS:Olaparib:genotypeALC1 KO PARP1 KO 0.283060
## MMS:Olaparib:genotypePARP1 KO 0.352924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
cat("AIC: ", AIC(fit4))
## AIC: 2321.885
simres <- simulateResiduals(fittedModel = fit4)
plot(simres)
fit5 <- lm(Counts ~ poly(MMS,2)*poly(Olaparib,2)*genotype, data = dataset)
print(summary(fit5))
##
## Call:
## lm(formula = Counts ~ poly(MMS, 2) * poly(Olaparib, 2) * genotype,
## data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -565.74 -108.66 -13.36 66.81 798.36
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 1107.29 37.88
## poly(MMS, 2)1 -3328.30 524.92
## poly(MMS, 2)2 205.29 524.92
## poly(Olaparib, 2)1 -3571.07 524.92
## poly(Olaparib, 2)2 -652.71 524.92
## genotypeALC1 KO -440.50 53.57
## genotypeALC1 KO PARP1 KO -95.02 53.57
## genotypePARP1 KO -498.90 53.57
## poly(MMS, 2)1:poly(Olaparib, 2)1 -3386.19 7273.48
## poly(MMS, 2)2:poly(Olaparib, 2)1 294.71 7273.48
## poly(MMS, 2)1:poly(Olaparib, 2)2 2542.16 7273.48
## poly(MMS, 2)2:poly(Olaparib, 2)2 -1386.81 7273.48
## poly(MMS, 2)1:genotypeALC1 KO -933.68 742.35
## poly(MMS, 2)2:genotypeALC1 KO -98.66 742.35
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO -3032.87 742.35
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO 10.71 742.35
## poly(MMS, 2)1:genotypePARP1 KO -18.32 742.35
## poly(MMS, 2)2:genotypePARP1 KO -273.72 742.35
## poly(Olaparib, 2)1:genotypeALC1 KO -2449.82 742.35
## poly(Olaparib, 2)2:genotypeALC1 KO 1490.78 742.35
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 1495.51 742.35
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 833.05 742.35
## poly(Olaparib, 2)1:genotypePARP1 KO 2332.53 742.35
## poly(Olaparib, 2)2:genotypePARP1 KO 421.59 742.35
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO 33486.40 10286.26
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO -1021.01 10286.26
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO -498.63 10286.26
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO 3943.39 10286.26
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 6215.24 10286.26
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 572.82 10286.26
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 1285.62 10286.26
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO -34.31 10286.26
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypePARP1 KO 5375.51 10286.26
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypePARP1 KO -3068.70 10286.26
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypePARP1 KO 201.69 10286.26
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypePARP1 KO 245.28 10286.26
## t value Pr(>|t|)
## (Intercept) 29.229 < 2e-16 ***
## poly(MMS, 2)1 -6.341 2.35e-09 ***
## poly(MMS, 2)2 0.391 0.69626
## poly(Olaparib, 2)1 -6.803 2.07e-10 ***
## poly(Olaparib, 2)2 -1.243 0.21557
## genotypeALC1 KO -8.222 7.29e-14 ***
## genotypeALC1 KO PARP1 KO -1.774 0.07808 .
## genotypePARP1 KO -9.312 < 2e-16 ***
## poly(MMS, 2)1:poly(Olaparib, 2)1 -0.466 0.64218
## poly(MMS, 2)2:poly(Olaparib, 2)1 0.041 0.96773
## poly(MMS, 2)1:poly(Olaparib, 2)2 0.350 0.72718
## poly(MMS, 2)2:poly(Olaparib, 2)2 -0.191 0.84903
## poly(MMS, 2)1:genotypeALC1 KO -1.258 0.21037
## poly(MMS, 2)2:genotypeALC1 KO -0.133 0.89444
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO -4.086 7.01e-05 ***
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO 0.014 0.98851
## poly(MMS, 2)1:genotypePARP1 KO -0.025 0.98035
## poly(MMS, 2)2:genotypePARP1 KO -0.369 0.71283
## poly(Olaparib, 2)1:genotypeALC1 KO -3.300 0.00120 **
## poly(Olaparib, 2)2:genotypeALC1 KO 2.008 0.04635 *
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 2.015 0.04567 *
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 1.122 0.26351
## poly(Olaparib, 2)1:genotypePARP1 KO 3.142 0.00201 **
## poly(Olaparib, 2)2:genotypePARP1 KO 0.568 0.57091
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO 3.255 0.00139 **
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO -0.099 0.92106
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO -0.048 0.96140
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO 0.383 0.70197
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 0.604 0.54657
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 0.056 0.95566
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 0.125 0.90070
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO -0.003 0.99734
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypePARP1 KO 0.523 0.60200
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypePARP1 KO -0.298 0.76585
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypePARP1 KO 0.020 0.98438
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypePARP1 KO 0.024 0.98101
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 262.5 on 156 degrees of freedom
## Multiple R-squared: 0.8051, Adjusted R-squared: 0.7614
## F-statistic: 18.42 on 35 and 156 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit5))
## AIC: 2717.922
simres <- simulateResiduals(fittedModel = fit5)
plot(simres)
fit6 <- lm(NormCounts ~ poly(MMS,2)*poly(Olaparib,2)*genotype, data = dataset)
print(summary(fit6))
##
## Call:
## lm(formula = NormCounts ~ poly(MMS, 2) * poly(Olaparib, 2) *
## genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33727 -0.06003 0.00501 0.05045 0.69976
##
## Coefficients:
## Estimate
## (Intercept) 1.000e+00
## poly(MMS, 2)1 -3.038e+00
## poly(MMS, 2)2 1.925e-01
## poly(Olaparib, 2)1 -3.294e+00
## poly(Olaparib, 2)2 -5.470e-01
## genotypeALC1 KO 2.773e-16
## genotypeALC1 KO PARP1 KO -2.066e-17
## genotypePARP1 KO 3.446e-17
## poly(MMS, 2)1:poly(Olaparib, 2)1 -4.248e+00
## poly(MMS, 2)2:poly(Olaparib, 2)1 9.019e-01
## poly(MMS, 2)1:poly(Olaparib, 2)2 2.517e+00
## poly(MMS, 2)2:poly(Olaparib, 2)2 -9.963e-01
## poly(MMS, 2)1:genotypeALC1 KO -3.294e+00
## poly(MMS, 2)2:genotypeALC1 KO -7.475e-02
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO -3.076e+00
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO -7.290e-02
## poly(MMS, 2)1:genotypePARP1 KO -2.481e+00
## poly(MMS, 2)2:genotypePARP1 KO -3.025e-01
## poly(Olaparib, 2)1:genotypeALC1 KO -5.783e+00
## poly(Olaparib, 2)2:genotypeALC1 KO 1.898e+00
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 1.161e+00
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 7.599e-01
## poly(Olaparib, 2)1:genotypePARP1 KO 1.260e+00
## poly(Olaparib, 2)2:genotypePARP1 KO 1.645e-01
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO 4.957e+01
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO -1.743e+00
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO -7.012e-01
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO 5.733e+00
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 8.678e+00
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO -6.005e-01
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 8.786e-01
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO -5.600e-01
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypePARP1 KO 7.528e+00
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypePARP1 KO -5.409e+00
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypePARP1 KO 2.216e+00
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypePARP1 KO -8.950e-01
## Std. Error t value
## (Intercept) 1.767e-02 56.605
## poly(MMS, 2)1 2.448e-01 -12.410
## poly(MMS, 2)2 2.448e-01 0.786
## poly(Olaparib, 2)1 2.448e-01 -13.458
## poly(Olaparib, 2)2 2.448e-01 -2.235
## genotypeALC1 KO 2.498e-02 0.000
## genotypeALC1 KO PARP1 KO 2.498e-02 0.000
## genotypePARP1 KO 2.498e-02 0.000
## poly(MMS, 2)1:poly(Olaparib, 2)1 3.392e+00 -1.253
## poly(MMS, 2)2:poly(Olaparib, 2)1 3.392e+00 0.266
## poly(MMS, 2)1:poly(Olaparib, 2)2 3.392e+00 0.742
## poly(MMS, 2)2:poly(Olaparib, 2)2 3.392e+00 -0.294
## poly(MMS, 2)1:genotypeALC1 KO 3.462e-01 -9.514
## poly(MMS, 2)2:genotypeALC1 KO 3.462e-01 -0.216
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO 3.462e-01 -8.884
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO 3.462e-01 -0.211
## poly(MMS, 2)1:genotypePARP1 KO 3.462e-01 -7.168
## poly(MMS, 2)2:genotypePARP1 KO 3.462e-01 -0.874
## poly(Olaparib, 2)1:genotypeALC1 KO 3.462e-01 -16.704
## poly(Olaparib, 2)2:genotypeALC1 KO 3.462e-01 5.482
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 3.462e-01 3.354
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 3.462e-01 2.195
## poly(Olaparib, 2)1:genotypePARP1 KO 3.462e-01 3.638
## poly(Olaparib, 2)2:genotypePARP1 KO 3.462e-01 0.475
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO 4.797e+00 10.335
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO 4.797e+00 -0.363
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO 4.797e+00 -0.146
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO 4.797e+00 1.195
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 4.797e+00 1.809
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 4.797e+00 -0.125
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 4.797e+00 0.183
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 4.797e+00 -0.117
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypePARP1 KO 4.797e+00 1.569
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypePARP1 KO 4.797e+00 -1.128
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypePARP1 KO 4.797e+00 0.462
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypePARP1 KO 4.797e+00 -0.187
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(MMS, 2)1 < 2e-16 ***
## poly(MMS, 2)2 0.432903
## poly(Olaparib, 2)1 < 2e-16 ***
## poly(Olaparib, 2)2 0.026854 *
## genotypeALC1 KO 1.000000
## genotypeALC1 KO PARP1 KO 1.000000
## genotypePARP1 KO 1.000000
## poly(MMS, 2)1:poly(Olaparib, 2)1 0.212251
## poly(MMS, 2)2:poly(Olaparib, 2)1 0.790662
## poly(MMS, 2)1:poly(Olaparib, 2)2 0.459135
## poly(MMS, 2)2:poly(Olaparib, 2)2 0.769362
## poly(MMS, 2)1:genotypeALC1 KO < 2e-16 ***
## poly(MMS, 2)2:genotypeALC1 KO 0.829325
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO 1.47e-15 ***
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO 0.833492
## poly(MMS, 2)1:genotypePARP1 KO 2.86e-11 ***
## poly(MMS, 2)2:genotypePARP1 KO 0.383580
## poly(Olaparib, 2)1:genotypeALC1 KO < 2e-16 ***
## poly(Olaparib, 2)2:genotypeALC1 KO 1.66e-07 ***
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 0.001002 **
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 0.029642 *
## poly(Olaparib, 2)1:genotypePARP1 KO 0.000372 ***
## poly(Olaparib, 2)2:genotypePARP1 KO 0.635223
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO < 2e-16 ***
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO 0.716804
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO 0.883964
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO 0.233850
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 0.072347 .
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 0.900542
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 0.854916
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 0.907219
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypePARP1 KO 0.118582
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypePARP1 KO 0.261229
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypePARP1 KO 0.644697
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypePARP1 KO 0.852227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1224 on 156 degrees of freedom
## Multiple R-squared: 0.9614, Adjusted R-squared: 0.9528
## F-statistic: 111.1 on 35 and 156 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit6))
## AIC: -227.5884
simres <- simulateResiduals(fittedModel = fit6)
plot(simres)
fit7 <- lm(NormCounts2 ~ poly(MMS,2)*poly(Olaparib,2)*genotype, data = dataset)
print(summary(fit7))
##
## Call:
## lm(formula = NormCounts2 ~ poly(MMS, 2) * poly(Olaparib, 2) *
## genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.176276 -0.033812 0.002994 0.027257 0.229972
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.652490 0.008248
## poly(MMS, 2)1 -1.982081 0.114286
## poly(MMS, 2)2 0.125585 0.114286
## poly(Olaparib, 2)1 -2.149615 0.114286
## poly(Olaparib, 2)2 -0.356941 0.114286
## genotypeALC1 KO -0.323846 0.011664
## genotypeALC1 KO PARP1 KO -0.099232 0.011664
## genotypePARP1 KO -0.071779 0.011664
## poly(MMS, 2)1:poly(Olaparib, 2)1 -2.772074 1.583591
## poly(MMS, 2)2:poly(Olaparib, 2)1 0.588500 1.583591
## poly(MMS, 2)1:poly(Olaparib, 2)2 1.642428 1.583591
## poly(MMS, 2)2:poly(Olaparib, 2)2 -0.650055 1.583591
## poly(MMS, 2)1:genotypeALC1 KO -0.098663 0.161625
## poly(MMS, 2)2:genotypeALC1 KO -0.086897 0.161625
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO -1.400108 0.161625
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO -0.059431 0.161625
## poly(MMS, 2)1:genotypePARP1 KO -1.222918 0.161625
## poly(MMS, 2)2:genotypePARP1 KO -0.189474 0.161625
## poly(Olaparib, 2)1:genotypeALC1 KO -0.833512 0.161625
## poly(Olaparib, 2)2:genotypeALC1 KO 0.800812 0.161625
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 0.969218 0.161625
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 0.474688 0.161625
## poly(Olaparib, 2)1:genotypePARP1 KO 0.967921 0.161625
## poly(Olaparib, 2)2:genotypePARP1 KO 0.134821 0.161625
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO 17.668136 2.239536
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO -0.864960 2.239536
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO -1.045630 2.239536
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO 2.206714 2.239536
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 5.222996 2.239536
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO -0.421718 2.239536
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 0.236286 2.239536
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO -0.210946 2.239536
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypePARP1 KO 4.676650 2.239536
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypePARP1 KO -3.205714 2.239536
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypePARP1 KO 1.106375 2.239536
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypePARP1 KO -0.448244 2.239536
## t value Pr(>|t|)
## (Intercept) 79.110 < 2e-16 ***
## poly(MMS, 2)1 -17.343 < 2e-16 ***
## poly(MMS, 2)2 1.099 0.27352
## poly(Olaparib, 2)1 -18.809 < 2e-16 ***
## poly(Olaparib, 2)2 -3.123 0.00213 **
## genotypeALC1 KO -27.764 < 2e-16 ***
## genotypeALC1 KO PARP1 KO -8.507 1.37e-14 ***
## genotypePARP1 KO -6.154 6.12e-09 ***
## poly(MMS, 2)1:poly(Olaparib, 2)1 -1.750 0.08200 .
## poly(MMS, 2)2:poly(Olaparib, 2)1 0.372 0.71068
## poly(MMS, 2)1:poly(Olaparib, 2)2 1.037 0.30127
## poly(MMS, 2)2:poly(Olaparib, 2)2 -0.410 0.68201
## poly(MMS, 2)1:genotypeALC1 KO -0.610 0.54246
## poly(MMS, 2)2:genotypeALC1 KO -0.538 0.59159
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO -8.663 5.48e-15 ***
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO -0.368 0.71359
## poly(MMS, 2)1:genotypePARP1 KO -7.566 3.11e-12 ***
## poly(MMS, 2)2:genotypePARP1 KO -1.172 0.24286
## poly(Olaparib, 2)1:genotypeALC1 KO -5.157 7.51e-07 ***
## poly(Olaparib, 2)2:genotypeALC1 KO 4.955 1.87e-06 ***
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 5.997 1.35e-08 ***
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 2.937 0.00382 **
## poly(Olaparib, 2)1:genotypePARP1 KO 5.989 1.41e-08 ***
## poly(Olaparib, 2)2:genotypePARP1 KO 0.834 0.40546
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO 7.889 4.98e-13 ***
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO -0.386 0.69986
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO -0.467 0.64123
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO 0.985 0.32598
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 2.332 0.02097 *
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO -0.188 0.85088
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 0.106 0.91611
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO -0.094 0.92508
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypePARP1 KO 2.088 0.03840 *
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypePARP1 KO -1.431 0.15431
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypePARP1 KO 0.494 0.62199
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypePARP1 KO -0.200 0.84162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05714 on 156 degrees of freedom
## Multiple R-squared: 0.9668, Adjusted R-squared: 0.9594
## F-statistic: 129.8 on 35 and 156 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit7))
## AIC: -520.079
simres <- simulateResiduals(fittedModel = fit7)
plot(simres)
fit8 <- lmer(Counts ~ poly(MMS,2)*poly(Olaparib,2)*genotype + (1|UID), data = dataset)
print(summary(fit8))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(MMS, 2) * poly(Olaparib, 2) * genotype + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 1981.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1127 -0.3008 -0.0111 0.3594 3.7456
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 48909 221.2
## Residual 23738 154.1
## Number of obs: 192, groups: UID, 48
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1107.29 67.60
## poly(MMS, 2)1 -3328.30 308.14
## poly(MMS, 2)2 205.29 308.14
## poly(Olaparib, 2)1 -3571.07 936.75
## poly(Olaparib, 2)2 -652.71 936.75
## genotypeALC1 KO -440.50 95.61
## genotypeALC1 KO PARP1 KO -95.02 95.61
## genotypePARP1 KO -498.90 95.61
## poly(MMS, 2)1:poly(Olaparib, 2)1 -3386.19 4269.78
## poly(MMS, 2)2:poly(Olaparib, 2)1 294.71 4269.78
## poly(MMS, 2)1:poly(Olaparib, 2)2 2542.16 4269.78
## poly(MMS, 2)2:poly(Olaparib, 2)2 -1386.81 4269.78
## poly(MMS, 2)1:genotypeALC1 KO -933.68 435.78
## poly(MMS, 2)2:genotypeALC1 KO -98.66 435.78
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO -3032.87 435.78
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO 10.71 435.78
## poly(MMS, 2)1:genotypePARP1 KO -18.32 435.78
## poly(MMS, 2)2:genotypePARP1 KO -273.72 435.78
## poly(Olaparib, 2)1:genotypeALC1 KO -2449.82 1324.76
## poly(Olaparib, 2)2:genotypeALC1 KO 1490.78 1324.76
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 1495.51 1324.76
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 833.05 1324.76
## poly(Olaparib, 2)1:genotypePARP1 KO 2332.53 1324.76
## poly(Olaparib, 2)2:genotypePARP1 KO 421.59 1324.76
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO 33486.40 6038.38
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO -1021.01 6038.38
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO -498.63 6038.38
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO 3943.39 6038.38
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 6215.24 6038.38
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 572.82 6038.38
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 1285.62 6038.38
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO -34.31 6038.38
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypePARP1 KO 5375.51 6038.38
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypePARP1 KO -3068.70 6038.38
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypePARP1 KO 201.69 6038.38
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypePARP1 KO 245.28 6038.38
## df t value
## (Intercept) 36.00 16.379
## poly(MMS, 2)1 120.00 -10.801
## poly(MMS, 2)2 120.00 0.666
## poly(Olaparib, 2)1 36.00 -3.812
## poly(Olaparib, 2)2 36.00 -0.697
## genotypeALC1 KO 36.00 -4.607
## genotypeALC1 KO PARP1 KO 36.00 -0.994
## genotypePARP1 KO 36.00 -5.218
## poly(MMS, 2)1:poly(Olaparib, 2)1 120.00 -0.793
## poly(MMS, 2)2:poly(Olaparib, 2)1 120.00 0.069
## poly(MMS, 2)1:poly(Olaparib, 2)2 120.00 0.595
## poly(MMS, 2)2:poly(Olaparib, 2)2 120.00 -0.325
## poly(MMS, 2)1:genotypeALC1 KO 120.00 -2.143
## poly(MMS, 2)2:genotypeALC1 KO 120.00 -0.226
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO 120.00 -6.960
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO 120.00 0.025
## poly(MMS, 2)1:genotypePARP1 KO 120.00 -0.042
## poly(MMS, 2)2:genotypePARP1 KO 120.00 -0.628
## poly(Olaparib, 2)1:genotypeALC1 KO 36.00 -1.849
## poly(Olaparib, 2)2:genotypeALC1 KO 36.00 1.125
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 36.00 1.129
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 36.00 0.629
## poly(Olaparib, 2)1:genotypePARP1 KO 36.00 1.761
## poly(Olaparib, 2)2:genotypePARP1 KO 36.00 0.318
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO 120.00 5.546
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO 120.00 -0.169
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO 120.00 -0.083
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO 120.00 0.653
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 120.00 1.029
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 120.00 0.095
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 120.00 0.213
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 120.00 -0.006
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypePARP1 KO 120.00 0.890
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypePARP1 KO 120.00 -0.508
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypePARP1 KO 120.00 0.033
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypePARP1 KO 120.00 0.041
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(MMS, 2)1 < 2e-16 ***
## poly(MMS, 2)2 0.50655
## poly(Olaparib, 2)1 0.00052 ***
## poly(Olaparib, 2)2 0.49041
## genotypeALC1 KO 4.95e-05 ***
## genotypeALC1 KO PARP1 KO 0.32692
## genotypePARP1 KO 7.70e-06 ***
## poly(MMS, 2)1:poly(Olaparib, 2)1 0.42931
## poly(MMS, 2)2:poly(Olaparib, 2)1 0.94509
## poly(MMS, 2)1:poly(Olaparib, 2)2 0.55271
## poly(MMS, 2)2:poly(Olaparib, 2)2 0.74590
## poly(MMS, 2)1:genotypeALC1 KO 0.03417 *
## poly(MMS, 2)2:genotypeALC1 KO 0.82128
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO 1.94e-10 ***
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO 0.98044
## poly(MMS, 2)1:genotypePARP1 KO 0.96654
## poly(MMS, 2)2:genotypePARP1 KO 0.53112
## poly(Olaparib, 2)1:genotypeALC1 KO 0.07265 .
## poly(Olaparib, 2)2:genotypeALC1 KO 0.26790
## poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 0.26641
## poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 0.53343
## poly(Olaparib, 2)1:genotypePARP1 KO 0.08678 .
## poly(Olaparib, 2)2:genotypePARP1 KO 0.75214
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO 1.77e-07 ***
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO 0.86601
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO 0.93433
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO 0.51497
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 0.30541
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypeALC1 KO PARP1 KO 0.92458
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 0.83176
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypeALC1 KO PARP1 KO 0.99548
## poly(MMS, 2)1:poly(Olaparib, 2)1:genotypePARP1 KO 0.37513
## poly(MMS, 2)2:poly(Olaparib, 2)1:genotypePARP1 KO 0.61225
## poly(MMS, 2)1:poly(Olaparib, 2)2:genotypePARP1 KO 0.97341
## poly(MMS, 2)2:poly(Olaparib, 2)2:genotypePARP1 KO 0.96767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("AIC: ", AIC(fit8))
## AIC: 2057.441
simres <- simulateResiduals(fittedModel = fit8)
plot(simres)
fit9 <- lm(Counts ~ poly(MMS,3)*poly(Olaparib,3)*genotype, data = dataset)
print(summary(fit9))
##
## Call:
## lm(formula = Counts ~ poly(MMS, 3) * poly(Olaparib, 3) * genotype,
## data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -554.00 -109.83 -15.50 59.67 834.00
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 1107.29 41.20
## poly(MMS, 3)1 -3328.30 570.86
## poly(MMS, 3)2 205.29 570.86
## poly(MMS, 3)3 56.44 570.86
## poly(Olaparib, 3)1 -3571.07 570.86
## poly(Olaparib, 3)2 -652.71 570.86
## poly(Olaparib, 3)3 192.56 570.86
## genotypeALC1 KO -440.50 58.26
## genotypeALC1 KO PARP1 KO -95.02 58.26
## genotypePARP1 KO -498.90 58.26
## poly(MMS, 3)1:poly(Olaparib, 3)1 -3386.19 7910.00
## poly(MMS, 3)2:poly(Olaparib, 3)1 294.71 7910.00
## poly(MMS, 3)3:poly(Olaparib, 3)1 130.48 7910.00
## poly(MMS, 3)1:poly(Olaparib, 3)2 2542.16 7910.00
## poly(MMS, 3)2:poly(Olaparib, 3)2 -1386.81 7910.00
## poly(MMS, 3)3:poly(Olaparib, 3)2 1300.58 7910.00
## poly(MMS, 3)1:poly(Olaparib, 3)3 1588.63 7910.00
## poly(MMS, 3)2:poly(Olaparib, 3)3 -2319.15 7910.00
## poly(MMS, 3)3:poly(Olaparib, 3)3 -421.09 7910.00
## poly(MMS, 3)1:genotypeALC1 KO -933.68 807.31
## poly(MMS, 3)2:genotypeALC1 KO -98.66 807.31
## poly(MMS, 3)3:genotypeALC1 KO -58.38 807.31
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO -3032.87 807.31
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO 10.71 807.31
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO 745.76 807.31
## poly(MMS, 3)1:genotypePARP1 KO -18.32 807.31
## poly(MMS, 3)2:genotypePARP1 KO -273.72 807.31
## poly(MMS, 3)3:genotypePARP1 KO 20.96 807.31
## poly(Olaparib, 3)1:genotypeALC1 KO -2449.82 807.31
## poly(Olaparib, 3)2:genotypeALC1 KO 1490.78 807.31
## poly(Olaparib, 3)3:genotypeALC1 KO -301.66 807.31
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 1495.51 807.31
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 833.05 807.31
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -197.92 807.31
## poly(Olaparib, 3)1:genotypePARP1 KO 2332.53 807.31
## poly(Olaparib, 3)2:genotypePARP1 KO 421.59 807.31
## poly(Olaparib, 3)3:genotypePARP1 KO -153.03 807.31
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO 33486.40 11186.43
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO -1021.01 11186.43
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO -1199.24 11186.43
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO -498.63 11186.43
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO 3943.39 11186.43
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO 2162.96 11186.43
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO -7062.78 11186.43
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO 6446.72 11186.43
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO -2324.73 11186.43
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 6215.24 11186.43
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 572.82 11186.43
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 4370.53 11186.43
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 1285.62 11186.43
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -34.31 11186.43
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -1890.79 11186.43
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -3477.78 11186.43
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 2036.67 11186.43
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -380.31 11186.43
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypePARP1 KO 5375.51 11186.43
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypePARP1 KO -3068.70 11186.43
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypePARP1 KO 1362.83 11186.43
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypePARP1 KO 201.69 11186.43
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypePARP1 KO 245.28 11186.43
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypePARP1 KO -1381.99 11186.43
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypePARP1 KO -595.31 11186.43
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypePARP1 KO 4218.88 11186.43
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypePARP1 KO 421.76 11186.43
## t value Pr(>|t|)
## (Intercept) 26.877 < 2e-16 ***
## poly(MMS, 3)1 -5.830 4.25e-08 ***
## poly(MMS, 3)2 0.360 0.71972
## poly(MMS, 3)3 0.099 0.92139
## poly(Olaparib, 3)1 -6.256 5.47e-09 ***
## poly(Olaparib, 3)2 -1.143 0.25501
## poly(Olaparib, 3)3 0.337 0.73642
## genotypeALC1 KO -7.561 6.82e-12 ***
## genotypeALC1 KO PARP1 KO -1.631 0.10537
## genotypePARP1 KO -8.563 2.97e-14 ***
## poly(MMS, 3)1:poly(Olaparib, 3)1 -0.428 0.66930
## poly(MMS, 3)2:poly(Olaparib, 3)1 0.037 0.97034
## poly(MMS, 3)3:poly(Olaparib, 3)1 0.016 0.98687
## poly(MMS, 3)1:poly(Olaparib, 3)2 0.321 0.74844
## poly(MMS, 3)2:poly(Olaparib, 3)2 -0.175 0.86110
## poly(MMS, 3)3:poly(Olaparib, 3)2 0.164 0.86966
## poly(MMS, 3)1:poly(Olaparib, 3)3 0.201 0.84114
## poly(MMS, 3)2:poly(Olaparib, 3)3 -0.293 0.76985
## poly(MMS, 3)3:poly(Olaparib, 3)3 -0.053 0.95763
## poly(MMS, 3)1:genotypeALC1 KO -1.157 0.24962
## poly(MMS, 3)2:genotypeALC1 KO -0.122 0.90293
## poly(MMS, 3)3:genotypeALC1 KO -0.072 0.94246
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO -3.757 0.00026 ***
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO 0.013 0.98944
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO 0.924 0.35735
## poly(MMS, 3)1:genotypePARP1 KO -0.023 0.98193
## poly(MMS, 3)2:genotypePARP1 KO -0.339 0.73513
## poly(MMS, 3)3:genotypePARP1 KO 0.026 0.97933
## poly(Olaparib, 3)1:genotypeALC1 KO -3.035 0.00292 **
## poly(Olaparib, 3)2:genotypeALC1 KO 1.847 0.06711 .
## poly(Olaparib, 3)3:genotypeALC1 KO -0.374 0.70928
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 1.852 0.06626 .
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 1.032 0.30407
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -0.245 0.80673
## poly(Olaparib, 3)1:genotypePARP1 KO 2.889 0.00454 **
## poly(Olaparib, 3)2:genotypePARP1 KO 0.522 0.60242
## poly(Olaparib, 3)3:genotypePARP1 KO -0.190 0.84996
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO 2.993 0.00331 **
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO -0.091 0.92742
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO -0.107 0.91479
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO -0.045 0.96452
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO 0.353 0.72503
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO 0.193 0.84699
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO -0.631 0.52892
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO 0.576 0.56543
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO -0.208 0.83570
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.556 0.57945
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.051 0.95924
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.391 0.69667
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.115 0.90868
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -0.003 0.99756
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -0.169 0.86604
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -0.311 0.75639
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 0.182 0.85582
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -0.034 0.97293
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypePARP1 KO 0.481 0.63166
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypePARP1 KO -0.274 0.78428
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypePARP1 KO 0.122 0.90323
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypePARP1 KO 0.018 0.98564
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypePARP1 KO 0.022 0.98254
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypePARP1 KO -0.124 0.90187
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypePARP1 KO -0.053 0.95764
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypePARP1 KO 0.377 0.70669
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypePARP1 KO 0.038 0.96998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 285.4 on 128 degrees of freedom
## Multiple R-squared: 0.8109, Adjusted R-squared: 0.7178
## F-statistic: 8.713 on 63 and 128 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit9))
## AIC: 2768.155
simres <- simulateResiduals(fittedModel = fit9)
plot(simres)
fit10 <- lm(NormCounts ~ poly(MMS,3)*poly(Olaparib,3)*genotype, data = dataset)
print(summary(fit10))
##
## Call:
## lm(formula = NormCounts ~ poly(MMS, 3) * poly(Olaparib, 3) *
## genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.44057 -0.05015 0.00013 0.04718 0.59646
##
## Coefficients:
## Estimate
## (Intercept) 1.000e+00
## poly(MMS, 3)1 -3.038e+00
## poly(MMS, 3)2 1.925e-01
## poly(MMS, 3)3 2.882e-02
## poly(Olaparib, 3)1 -3.294e+00
## poly(Olaparib, 3)2 -5.470e-01
## poly(Olaparib, 3)3 2.354e-01
## genotypeALC1 KO 1.488e-16
## genotypeALC1 KO PARP1 KO -2.135e-16
## genotypePARP1 KO 1.954e-17
## poly(MMS, 3)1:poly(Olaparib, 3)1 -4.248e+00
## poly(MMS, 3)2:poly(Olaparib, 3)1 9.019e-01
## poly(MMS, 3)3:poly(Olaparib, 3)1 -7.839e-02
## poly(MMS, 3)1:poly(Olaparib, 3)2 2.517e+00
## poly(MMS, 3)2:poly(Olaparib, 3)2 -9.963e-01
## poly(MMS, 3)3:poly(Olaparib, 3)2 1.164e+00
## poly(MMS, 3)1:poly(Olaparib, 3)3 1.649e+00
## poly(MMS, 3)2:poly(Olaparib, 3)3 -2.059e+00
## poly(MMS, 3)3:poly(Olaparib, 3)3 -3.591e-01
## poly(MMS, 3)1:genotypeALC1 KO -3.294e+00
## poly(MMS, 3)2:genotypeALC1 KO -7.475e-02
## poly(MMS, 3)3:genotypeALC1 KO 4.203e-02
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO -3.076e+00
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO -7.290e-02
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO 8.635e-01
## poly(MMS, 3)1:genotypePARP1 KO -2.481e+00
## poly(MMS, 3)2:genotypePARP1 KO -3.025e-01
## poly(MMS, 3)3:genotypePARP1 KO 6.485e-02
## poly(Olaparib, 3)1:genotypeALC1 KO -5.783e+00
## poly(Olaparib, 3)2:genotypeALC1 KO 1.898e+00
## poly(Olaparib, 3)3:genotypeALC1 KO -4.394e-01
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 1.161e+00
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 7.599e-01
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -2.558e-01
## poly(Olaparib, 3)1:genotypePARP1 KO 1.260e+00
## poly(Olaparib, 3)2:genotypePARP1 KO 1.645e-01
## poly(Olaparib, 3)3:genotypePARP1 KO -1.662e-01
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO 4.957e+01
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO -1.743e+00
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO -2.668e+00
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO -7.012e-01
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO 5.733e+00
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO 3.716e+00
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO -9.138e+00
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO 7.724e+00
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO -3.223e+00
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 8.678e+00
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO -6.005e-01
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 4.810e+00
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 8.786e-01
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -5.600e-01
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -1.881e+00
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -3.606e+00
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 2.123e+00
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -3.858e-01
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypePARP1 KO 7.528e+00
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypePARP1 KO -5.409e+00
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypePARP1 KO 2.594e+00
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypePARP1 KO 2.216e+00
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypePARP1 KO -8.950e-01
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypePARP1 KO -1.114e+00
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypePARP1 KO -1.330e-01
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypePARP1 KO 5.157e+00
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypePARP1 KO 2.856e-01
## Std. Error t value
## (Intercept) 1.743e-02 57.380
## poly(MMS, 3)1 2.415e-01 -12.579
## poly(MMS, 3)2 2.415e-01 0.797
## poly(MMS, 3)3 2.415e-01 0.119
## poly(Olaparib, 3)1 2.415e-01 -13.643
## poly(Olaparib, 3)2 2.415e-01 -2.265
## poly(Olaparib, 3)3 2.415e-01 0.975
## genotypeALC1 KO 2.465e-02 0.000
## genotypeALC1 KO PARP1 KO 2.465e-02 0.000
## genotypePARP1 KO 2.465e-02 0.000
## poly(MMS, 3)1:poly(Olaparib, 3)1 3.346e+00 -1.270
## poly(MMS, 3)2:poly(Olaparib, 3)1 3.346e+00 0.270
## poly(MMS, 3)3:poly(Olaparib, 3)1 3.346e+00 -0.023
## poly(MMS, 3)1:poly(Olaparib, 3)2 3.346e+00 0.752
## poly(MMS, 3)2:poly(Olaparib, 3)2 3.346e+00 -0.298
## poly(MMS, 3)3:poly(Olaparib, 3)2 3.346e+00 0.348
## poly(MMS, 3)1:poly(Olaparib, 3)3 3.346e+00 0.493
## poly(MMS, 3)2:poly(Olaparib, 3)3 3.346e+00 -0.615
## poly(MMS, 3)3:poly(Olaparib, 3)3 3.346e+00 -0.107
## poly(MMS, 3)1:genotypeALC1 KO 3.415e-01 -9.644
## poly(MMS, 3)2:genotypeALC1 KO 3.415e-01 -0.219
## poly(MMS, 3)3:genotypeALC1 KO 3.415e-01 0.123
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO 3.415e-01 -9.006
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO 3.415e-01 -0.213
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO 3.415e-01 2.528
## poly(MMS, 3)1:genotypePARP1 KO 3.415e-01 -7.266
## poly(MMS, 3)2:genotypePARP1 KO 3.415e-01 -0.886
## poly(MMS, 3)3:genotypePARP1 KO 3.415e-01 0.190
## poly(Olaparib, 3)1:genotypeALC1 KO 3.415e-01 -16.932
## poly(Olaparib, 3)2:genotypeALC1 KO 3.415e-01 5.557
## poly(Olaparib, 3)3:genotypeALC1 KO 3.415e-01 -1.287
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 3.415e-01 3.399
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 3.415e-01 2.225
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 3.415e-01 -0.749
## poly(Olaparib, 3)1:genotypePARP1 KO 3.415e-01 3.688
## poly(Olaparib, 3)2:genotypePARP1 KO 3.415e-01 0.482
## poly(Olaparib, 3)3:genotypePARP1 KO 3.415e-01 -0.487
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO 4.732e+00 10.476
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO 4.732e+00 -0.368
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO 4.732e+00 -0.564
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO 4.732e+00 -0.148
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO 4.732e+00 1.211
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO 4.732e+00 0.785
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO 4.732e+00 -1.931
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO 4.732e+00 1.632
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO 4.732e+00 -0.681
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 4.732e+00 1.834
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 4.732e+00 -0.127
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 4.732e+00 1.016
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 4.732e+00 0.186
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 4.732e+00 -0.118
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 4.732e+00 -0.398
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 4.732e+00 -0.762
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 4.732e+00 0.449
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 4.732e+00 -0.082
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypePARP1 KO 4.732e+00 1.591
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypePARP1 KO 4.732e+00 -1.143
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypePARP1 KO 4.732e+00 0.548
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypePARP1 KO 4.732e+00 0.468
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypePARP1 KO 4.732e+00 -0.189
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypePARP1 KO 4.732e+00 -0.235
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypePARP1 KO 4.732e+00 -0.028
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypePARP1 KO 4.732e+00 1.090
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypePARP1 KO 4.732e+00 0.060
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(MMS, 3)1 < 2e-16 ***
## poly(MMS, 3)2 0.426909
## poly(MMS, 3)3 0.905198
## poly(Olaparib, 3)1 < 2e-16 ***
## poly(Olaparib, 3)2 0.025172 *
## poly(Olaparib, 3)3 0.331400
## genotypeALC1 KO 1.000000
## genotypeALC1 KO PARP1 KO 1.000000
## genotypePARP1 KO 1.000000
## poly(MMS, 3)1:poly(Olaparib, 3)1 0.206504
## poly(MMS, 3)2:poly(Olaparib, 3)1 0.787943
## poly(MMS, 3)3:poly(Olaparib, 3)1 0.981347
## poly(MMS, 3)1:poly(Olaparib, 3)2 0.453270
## poly(MMS, 3)2:poly(Olaparib, 3)2 0.766384
## poly(MMS, 3)3:poly(Olaparib, 3)2 0.728591
## poly(MMS, 3)1:poly(Olaparib, 3)3 0.623032
## poly(MMS, 3)2:poly(Olaparib, 3)3 0.539329
## poly(MMS, 3)3:poly(Olaparib, 3)3 0.914712
## poly(MMS, 3)1:genotypeALC1 KO < 2e-16 ***
## poly(MMS, 3)2:genotypeALC1 KO 0.827089
## poly(MMS, 3)3:genotypeALC1 KO 0.902233
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO 2.55e-15 ***
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO 0.831309
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO 0.012672 *
## poly(MMS, 3)1:genotypePARP1 KO 3.23e-11 ***
## poly(MMS, 3)2:genotypePARP1 KO 0.377418
## poly(MMS, 3)3:genotypePARP1 KO 0.849705
## poly(Olaparib, 3)1:genotypeALC1 KO < 2e-16 ***
## poly(Olaparib, 3)2:genotypeALC1 KO 1.53e-07 ***
## poly(Olaparib, 3)3:genotypeALC1 KO 0.200548
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.000901 ***
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.027831 *
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 0.455257
## poly(Olaparib, 3)1:genotypePARP1 KO 0.000333 ***
## poly(Olaparib, 3)2:genotypePARP1 KO 0.630752
## poly(Olaparib, 3)3:genotypePARP1 KO 0.627231
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO < 2e-16 ***
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO 0.713209
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO 0.573939
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO 0.882430
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO 0.227940
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO 0.433744
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO 0.055672 .
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO 0.105089
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO 0.497080
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.068984 .
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.899223
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.311314
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.853006
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.905988
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.691645
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 0.447499
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 0.654485
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 0.935148
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypePARP1 KO 0.114104
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypePARP1 KO 0.255167
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypePARP1 KO 0.584556
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypePARP1 KO 0.640321
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypePARP1 KO 0.850282
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypePARP1 KO 0.814328
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypePARP1 KO 0.977626
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypePARP1 KO 0.277852
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypePARP1 KO 0.951975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1207 on 128 degrees of freedom
## Multiple R-squared: 0.9692, Adjusted R-squared: 0.954
## F-statistic: 63.91 on 63 and 128 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit10))
## AIC: -214.7912
simres <- simulateResiduals(fittedModel = fit10)
plot(simres)
fit11 <- lm(NormCounts2 ~ poly(MMS,3)*poly(Olaparib,3)*genotype, data = dataset)
print(summary(fit11))
##
## Call:
## lm(formula = NormCounts2 ~ poly(MMS, 3) * poly(Olaparib, 3) *
## genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.154847 -0.029793 0.000046 0.022533 0.196022
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.652490 0.008047
## poly(MMS, 3)1 -1.982081 0.111504
## poly(MMS, 3)2 0.125585 0.111504
## poly(MMS, 3)3 0.018803 0.111504
## poly(Olaparib, 3)1 -2.149615 0.111504
## poly(Olaparib, 3)2 -0.356941 0.111504
## poly(Olaparib, 3)3 0.153626 0.111504
## genotypeALC1 KO -0.323846 0.011380
## genotypeALC1 KO PARP1 KO -0.099232 0.011380
## genotypePARP1 KO -0.071779 0.011380
## poly(MMS, 3)1:poly(Olaparib, 3)1 -2.772074 1.545040
## poly(MMS, 3)2:poly(Olaparib, 3)1 0.588500 1.545040
## poly(MMS, 3)3:poly(Olaparib, 3)1 -0.051146 1.545040
## poly(MMS, 3)1:poly(Olaparib, 3)2 1.642428 1.545040
## poly(MMS, 3)2:poly(Olaparib, 3)2 -0.650055 1.545040
## poly(MMS, 3)3:poly(Olaparib, 3)2 0.759265 1.545040
## poly(MMS, 3)1:poly(Olaparib, 3)3 1.075823 1.545040
## poly(MMS, 3)2:poly(Olaparib, 3)3 -1.343780 1.545040
## poly(MMS, 3)3:poly(Olaparib, 3)3 -0.234287 1.545040
## poly(MMS, 3)1:genotypeALC1 KO -0.098663 0.157690
## poly(MMS, 3)2:genotypeALC1 KO -0.086897 0.157690
## poly(MMS, 3)3:genotypeALC1 KO 0.004482 0.157690
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO -1.400108 0.157690
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO -0.059431 0.157690
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO 0.474874 0.157690
## poly(MMS, 3)1:genotypePARP1 KO -1.222918 0.157690
## poly(MMS, 3)2:genotypePARP1 KO -0.189474 0.157690
## poly(MMS, 3)3:genotypePARP1 KO 0.035588 0.157690
## poly(Olaparib, 3)1:genotypeALC1 KO -0.833512 0.157690
## poly(Olaparib, 3)2:genotypeALC1 KO 0.800812 0.157690
## poly(Olaparib, 3)3:genotypeALC1 KO -0.220653 0.157690
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.969218 0.157690
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.474688 0.157690
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -0.164874 0.157690
## poly(Olaparib, 3)1:genotypePARP1 KO 0.967921 0.157690
## poly(Olaparib, 3)2:genotypePARP1 KO 0.134821 0.157690
## poly(Olaparib, 3)3:genotypePARP1 KO -0.113442 0.157690
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO 17.668136 2.185017
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO -0.864960 2.185017
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO -0.851279 2.185017
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO -1.045630 2.185017
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO 2.206714 2.185017
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO 0.844398 2.185017
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO -3.537268 2.185017
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO 3.205352 2.185017
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO -0.942846 2.185017
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 5.222996 2.185017
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO -0.421718 2.185017
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 2.669019 2.185017
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.236286 2.185017
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -0.210946 2.185017
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -1.156209 2.185017
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -2.158421 2.185017
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 1.378809 2.185017
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -0.177819 2.185017
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypePARP1 KO 4.676650 2.185017
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypePARP1 KO -3.205714 2.185017
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypePARP1 KO 1.511881 2.185017
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypePARP1 KO 1.106375 2.185017
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypePARP1 KO -0.448244 2.185017
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypePARP1 KO -0.730210 2.185017
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypePARP1 KO -0.195567 2.185017
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypePARP1 KO 3.142555 2.185017
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypePARP1 KO 0.191598 2.185017
## t value Pr(>|t|)
## (Intercept) 81.084 < 2e-16 ***
## poly(MMS, 3)1 -17.776 < 2e-16 ***
## poly(MMS, 3)2 1.126 0.26215
## poly(MMS, 3)3 0.169 0.86635
## poly(Olaparib, 3)1 -19.278 < 2e-16 ***
## poly(Olaparib, 3)2 -3.201 0.00173 **
## poly(Olaparib, 3)3 1.378 0.17068
## genotypeALC1 KO -28.457 < 2e-16 ***
## genotypeALC1 KO PARP1 KO -8.720 1.25e-14 ***
## genotypePARP1 KO -6.307 4.24e-09 ***
## poly(MMS, 3)1:poly(Olaparib, 3)1 -1.794 0.07515 .
## poly(MMS, 3)2:poly(Olaparib, 3)1 0.381 0.70391
## poly(MMS, 3)3:poly(Olaparib, 3)1 -0.033 0.97364
## poly(MMS, 3)1:poly(Olaparib, 3)2 1.063 0.28977
## poly(MMS, 3)2:poly(Olaparib, 3)2 -0.421 0.67465
## poly(MMS, 3)3:poly(Olaparib, 3)2 0.491 0.62397
## poly(MMS, 3)1:poly(Olaparib, 3)3 0.696 0.48750
## poly(MMS, 3)2:poly(Olaparib, 3)3 -0.870 0.38607
## poly(MMS, 3)3:poly(Olaparib, 3)3 -0.152 0.87971
## poly(MMS, 3)1:genotypeALC1 KO -0.626 0.53264
## poly(MMS, 3)2:genotypeALC1 KO -0.551 0.58255
## poly(MMS, 3)3:genotypeALC1 KO 0.028 0.97737
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO -8.879 5.17e-15 ***
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO -0.377 0.70688
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO 3.011 0.00313 **
## poly(MMS, 3)1:genotypePARP1 KO -7.755 2.41e-12 ***
## poly(MMS, 3)2:genotypePARP1 KO -1.202 0.23175
## poly(MMS, 3)3:genotypePARP1 KO 0.226 0.82181
## poly(Olaparib, 3)1:genotypeALC1 KO -5.286 5.24e-07 ***
## poly(Olaparib, 3)2:genotypeALC1 KO 5.078 1.31e-06 ***
## poly(Olaparib, 3)3:genotypeALC1 KO -1.399 0.16415
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 6.146 9.33e-09 ***
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 3.010 0.00315 **
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -1.046 0.29774
## poly(Olaparib, 3)1:genotypePARP1 KO 6.138 9.71e-09 ***
## poly(Olaparib, 3)2:genotypePARP1 KO 0.855 0.39416
## poly(Olaparib, 3)3:genotypePARP1 KO -0.719 0.47321
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO 8.086 4.05e-13 ***
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO -0.396 0.69287
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO -0.390 0.69748
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO -0.479 0.63308
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO 1.010 0.31443
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO 0.386 0.69981
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO -1.619 0.10794
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO 1.467 0.14484
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO -0.432 0.66683
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 2.390 0.01829 *
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO -0.193 0.84726
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 1.222 0.22414
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.108 0.91405
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -0.097 0.92324
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -0.529 0.59762
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -0.988 0.32510
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 0.631 0.52915
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -0.081 0.93527
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypePARP1 KO 2.140 0.03422 *
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypePARP1 KO -1.467 0.14479
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypePARP1 KO 0.692 0.49023
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypePARP1 KO 0.506 0.61348
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypePARP1 KO -0.205 0.83779
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypePARP1 KO -0.334 0.73878
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypePARP1 KO -0.090 0.92882
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypePARP1 KO 1.438 0.15281
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypePARP1 KO 0.088 0.93026
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05575 on 128 degrees of freedom
## Multiple R-squared: 0.9741, Adjusted R-squared: 0.9613
## F-statistic: 76.32 on 63 and 128 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit11))
## AIC: -511.5253
simres <- simulateResiduals(fittedModel = fit11)
plot(simres)
fit12 <- lmer(Counts ~ poly(MMS,3)*poly(Olaparib,3)*genotype + (1|UID), data = dataset)
print(summary(fit12))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(MMS, 3) * poly(Olaparib, 3) * genotype + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 1481.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8984 -0.2742 -0.0202 0.3026 3.1715
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 54977 234.5
## Residual 26492 162.8
## Number of obs: 192, groups: UID, 48
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1107.29 71.65
## poly(MMS, 3)1 -3328.30 325.53
## poly(MMS, 3)2 205.29 325.53
## poly(MMS, 3)3 56.44 325.53
## poly(Olaparib, 3)1 -3571.07 992.77
## poly(Olaparib, 3)2 -652.71 992.77
## poly(Olaparib, 3)3 192.56 992.77
## genotypeALC1 KO -440.50 101.32
## genotypeALC1 KO PARP1 KO -95.02 101.32
## genotypePARP1 KO -498.90 101.32
## poly(MMS, 3)1:poly(Olaparib, 3)1 -3386.19 4510.64
## poly(MMS, 3)2:poly(Olaparib, 3)1 294.71 4510.64
## poly(MMS, 3)3:poly(Olaparib, 3)1 130.48 4510.64
## poly(MMS, 3)1:poly(Olaparib, 3)2 2542.16 4510.64
## poly(MMS, 3)2:poly(Olaparib, 3)2 -1386.81 4510.64
## poly(MMS, 3)3:poly(Olaparib, 3)2 1300.58 4510.64
## poly(MMS, 3)1:poly(Olaparib, 3)3 1588.63 4510.64
## poly(MMS, 3)2:poly(Olaparib, 3)3 -2319.15 4510.64
## poly(MMS, 3)3:poly(Olaparib, 3)3 -421.09 4510.64
## poly(MMS, 3)1:genotypeALC1 KO -933.68 460.37
## poly(MMS, 3)2:genotypeALC1 KO -98.66 460.37
## poly(MMS, 3)3:genotypeALC1 KO -58.38 460.37
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO -3032.87 460.37
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO 10.71 460.37
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO 745.76 460.37
## poly(MMS, 3)1:genotypePARP1 KO -18.32 460.37
## poly(MMS, 3)2:genotypePARP1 KO -273.72 460.37
## poly(MMS, 3)3:genotypePARP1 KO 20.96 460.37
## poly(Olaparib, 3)1:genotypeALC1 KO -2449.82 1403.99
## poly(Olaparib, 3)2:genotypeALC1 KO 1490.78 1403.99
## poly(Olaparib, 3)3:genotypeALC1 KO -301.66 1403.99
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 1495.51 1403.99
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 833.05 1403.99
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -197.92 1403.99
## poly(Olaparib, 3)1:genotypePARP1 KO 2332.53 1403.99
## poly(Olaparib, 3)2:genotypePARP1 KO 421.59 1403.99
## poly(Olaparib, 3)3:genotypePARP1 KO -153.03 1403.99
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO 33486.40 6379.01
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO -1021.01 6379.01
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO -1199.24 6379.01
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO -498.63 6379.01
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO 3943.39 6379.01
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO 2162.96 6379.01
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO -7062.78 6379.01
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO 6446.72 6379.01
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO -2324.73 6379.01
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 6215.24 6379.01
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 572.82 6379.01
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 4370.53 6379.01
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 1285.62 6379.01
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -34.31 6379.01
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO -1890.79 6379.01
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -3477.78 6379.01
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 2036.67 6379.01
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO -380.31 6379.01
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypePARP1 KO 5375.51 6379.01
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypePARP1 KO -3068.70 6379.01
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypePARP1 KO 1362.83 6379.01
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypePARP1 KO 201.69 6379.01
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypePARP1 KO 245.28 6379.01
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypePARP1 KO -1381.99 6379.01
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypePARP1 KO -595.31 6379.01
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypePARP1 KO 4218.88 6379.01
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypePARP1 KO 421.76 6379.01
## df t value
## (Intercept) 32.00 15.455
## poly(MMS, 3)1 96.00 -10.224
## poly(MMS, 3)2 96.00 0.631
## poly(MMS, 3)3 96.00 0.173
## poly(Olaparib, 3)1 32.00 -3.597
## poly(Olaparib, 3)2 32.00 -0.657
## poly(Olaparib, 3)3 32.00 0.194
## genotypeALC1 KO 32.00 -4.347
## genotypeALC1 KO PARP1 KO 32.00 -0.938
## genotypePARP1 KO 32.00 -4.924
## poly(MMS, 3)1:poly(Olaparib, 3)1 96.00 -0.751
## poly(MMS, 3)2:poly(Olaparib, 3)1 96.00 0.065
## poly(MMS, 3)3:poly(Olaparib, 3)1 96.00 0.029
## poly(MMS, 3)1:poly(Olaparib, 3)2 96.00 0.564
## poly(MMS, 3)2:poly(Olaparib, 3)2 96.00 -0.307
## poly(MMS, 3)3:poly(Olaparib, 3)2 96.00 0.288
## poly(MMS, 3)1:poly(Olaparib, 3)3 96.00 0.352
## poly(MMS, 3)2:poly(Olaparib, 3)3 96.00 -0.514
## poly(MMS, 3)3:poly(Olaparib, 3)3 96.00 -0.093
## poly(MMS, 3)1:genotypeALC1 KO 96.00 -2.028
## poly(MMS, 3)2:genotypeALC1 KO 96.00 -0.214
## poly(MMS, 3)3:genotypeALC1 KO 96.00 -0.127
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO 96.00 -6.588
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO 96.00 0.023
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO 96.00 1.620
## poly(MMS, 3)1:genotypePARP1 KO 96.00 -0.040
## poly(MMS, 3)2:genotypePARP1 KO 96.00 -0.595
## poly(MMS, 3)3:genotypePARP1 KO 96.00 0.046
## poly(Olaparib, 3)1:genotypeALC1 KO 32.00 -1.745
## poly(Olaparib, 3)2:genotypeALC1 KO 32.00 1.062
## poly(Olaparib, 3)3:genotypeALC1 KO 32.00 -0.215
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 32.00 1.065
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 32.00 0.593
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 32.00 -0.141
## poly(Olaparib, 3)1:genotypePARP1 KO 32.00 1.661
## poly(Olaparib, 3)2:genotypePARP1 KO 32.00 0.300
## poly(Olaparib, 3)3:genotypePARP1 KO 32.00 -0.109
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO 96.00 5.249
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO 96.00 -0.160
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO 96.00 -0.188
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO 96.00 -0.078
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO 96.00 0.618
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO 96.00 0.339
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO 96.00 -1.107
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO 96.00 1.011
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO 96.00 -0.364
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 96.00 0.974
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 96.00 0.090
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 96.00 0.685
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 96.00 0.202
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 96.00 -0.005
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 96.00 -0.296
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 96.00 -0.545
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 96.00 0.319
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 96.00 -0.060
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypePARP1 KO 96.00 0.843
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypePARP1 KO 96.00 -0.481
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypePARP1 KO 96.00 0.214
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypePARP1 KO 96.00 0.032
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypePARP1 KO 96.00 0.038
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypePARP1 KO 96.00 -0.217
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypePARP1 KO 96.00 -0.093
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypePARP1 KO 96.00 0.661
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypePARP1 KO 96.00 0.066
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(MMS, 3)1 < 2e-16 ***
## poly(MMS, 3)2 0.529771
## poly(MMS, 3)3 0.862708
## poly(Olaparib, 3)1 0.001070 **
## poly(Olaparib, 3)2 0.515587
## poly(Olaparib, 3)3 0.847428
## genotypeALC1 KO 0.000131 ***
## genotypeALC1 KO PARP1 KO 0.355378
## genotypePARP1 KO 2.48e-05 ***
## poly(MMS, 3)1:poly(Olaparib, 3)1 0.454662
## poly(MMS, 3)2:poly(Olaparib, 3)1 0.948043
## poly(MMS, 3)3:poly(Olaparib, 3)1 0.976983
## poly(MMS, 3)1:poly(Olaparib, 3)2 0.574346
## poly(MMS, 3)2:poly(Olaparib, 3)2 0.759165
## poly(MMS, 3)3:poly(Olaparib, 3)2 0.773710
## poly(MMS, 3)1:poly(Olaparib, 3)3 0.725462
## poly(MMS, 3)2:poly(Olaparib, 3)3 0.608328
## poly(MMS, 3)3:poly(Olaparib, 3)3 0.925816
## poly(MMS, 3)1:genotypeALC1 KO 0.045319 *
## poly(MMS, 3)2:genotypeALC1 KO 0.830764
## poly(MMS, 3)3:genotypeALC1 KO 0.899350
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO 2.39e-09 ***
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO 0.981490
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO 0.108526
## poly(MMS, 3)1:genotypePARP1 KO 0.968345
## poly(MMS, 3)2:genotypePARP1 KO 0.553527
## poly(MMS, 3)3:genotypePARP1 KO 0.963779
## poly(Olaparib, 3)1:genotypeALC1 KO 0.090602 .
## poly(Olaparib, 3)2:genotypeALC1 KO 0.296268
## poly(Olaparib, 3)3:genotypeALC1 KO 0.831243
## poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.294764
## poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.557120
## poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 0.888778
## poly(Olaparib, 3)1:genotypePARP1 KO 0.106410
## poly(Olaparib, 3)2:genotypePARP1 KO 0.765907
## poly(Olaparib, 3)3:genotypePARP1 KO 0.913886
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO 9.13e-07 ***
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO 0.873172
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO 0.851276
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO 0.937858
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO 0.537919
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO 0.735294
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO 0.270978
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO 0.314741
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO 0.716336
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.332343
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.928635
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypeALC1 KO PARP1 KO 0.494905
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.840703
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.995720
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypeALC1 KO PARP1 KO 0.767559
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 0.586887
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 0.750211
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypeALC1 KO PARP1 KO 0.952583
## poly(MMS, 3)1:poly(Olaparib, 3)1:genotypePARP1 KO 0.401498
## poly(MMS, 3)2:poly(Olaparib, 3)1:genotypePARP1 KO 0.631567
## poly(MMS, 3)3:poly(Olaparib, 3)1:genotypePARP1 KO 0.831279
## poly(MMS, 3)1:poly(Olaparib, 3)2:genotypePARP1 KO 0.974842
## poly(MMS, 3)2:poly(Olaparib, 3)2:genotypePARP1 KO 0.969407
## poly(MMS, 3)3:poly(Olaparib, 3)2:genotypePARP1 KO 0.828944
## poly(MMS, 3)1:poly(Olaparib, 3)3:genotypePARP1 KO 0.925841
## poly(MMS, 3)2:poly(Olaparib, 3)3:genotypePARP1 KO 0.509960
## poly(MMS, 3)3:poly(Olaparib, 3)3:genotypePARP1 KO 0.947422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("AIC: ", AIC(fit12))
## AIC: 1613.278
simres <- simulateResiduals(fittedModel = fit12)
plot(simres)
ICtab(fit1,fit2,fit3,fit4,
fit5,fit6,fit7,fit8,
fit9,fit10,fit11,fit12,
base=T)
## AIC dAIC df
## fit7 -520.1 0.0 37
## fit3 -514.2 5.9 17
## fit11 -511.5 8.6 65
## fit6 -227.6 292.5 37
## fit2 -215.9 304.2 17
## fit10 -214.8 305.3 65
## fit12 1613.3 2133.4 66
## fit8 2057.4 2577.5 38
## fit4 2321.9 2842.0 18
## fit1 2684.9 3205.0 17
## fit5 2717.9 3238.0 37
## fit9 2768.2 3288.2 65
fit <- fit7
output <- coef(summary(fit))
output <- output[grep("MMS|Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype", paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1], sep = " in " )
# suggested result table
kable(output, row.names = T)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| MMS1 in WT | -1.9820806 | 0.1142859 | -17.3431838 | 0.0000000 |
| MMS2 in WT | 0.1255846 | 0.1142859 | 1.0988643 | 0.2735203 |
| Olaparib1 in WT | -2.1496148 | 0.1142859 | -18.8091062 | 0.0000000 |
| Olaparib2 in WT | -0.3569409 | 0.1142859 | -3.1232292 | 0.0021328 |
| MMS1:Olaparib1 in WT | -2.7720737 | 1.5835913 | -1.7504982 | 0.0819983 |
| MMS2:Olaparib1 in WT | 0.5884996 | 1.5835913 | 0.3716234 | 0.7106775 |
| MMS1:Olaparib2 in WT | 1.6424281 | 1.5835913 | 1.0371540 | 0.3012690 |
| MMS2:Olaparib2 in WT | -0.6500546 | 1.5835913 | -0.4104939 | 0.6820069 |
| MMS1: WT vs. ALC1 KO | -0.0986626 | 0.1616246 | -0.6104431 | 0.5424566 |
| MMS2: WT vs. ALC1 KO | -0.0868971 | 0.1616246 | -0.5376476 | 0.5915865 |
| MMS1: WT vs. ALC1 KO PARP1 KO | -1.4001079 | 0.1616246 | -8.6627151 | 0.0000000 |
| MMS2: WT vs. ALC1 KO PARP1 KO | -0.0594307 | 0.1616246 | -0.3677083 | 0.7135892 |
| MMS1: WT vs. PARP1 KO | -1.2229177 | 0.1616246 | -7.5664079 | 0.0000000 |
| MMS2: WT vs. PARP1 KO | -0.1894742 | 0.1616246 | -1.1723102 | 0.2428597 |
| Olaparib1: WT vs. ALC1 KO | -0.8335123 | 0.1616246 | -5.1570878 | 0.0000008 |
| Olaparib2: WT vs. ALC1 KO | 0.8008124 | 0.1616246 | 4.9547675 | 0.0000019 |
| Olaparib1: WT vs. ALC1 KO PARP1 KO | 0.9692179 | 0.1616246 | 5.9967221 | 0.0000000 |
| Olaparib2: WT vs. ALC1 KO PARP1 KO | 0.4746879 | 0.1616246 | 2.9369777 | 0.0038166 |
| Olaparib1: WT vs. PARP1 KO | 0.9679212 | 0.1616246 | 5.9886992 | 0.0000000 |
| Olaparib2: WT vs. PARP1 KO | 0.1348212 | 0.1616246 | 0.8341628 | 0.4054648 |
| MMS1:Olaparib1: WT vs. ALC1 KO | 17.6681357 | 2.2395363 | 7.8891939 | 0.0000000 |
| MMS2:Olaparib1: WT vs. ALC1 KO | -0.8649597 | 2.2395363 | -0.3862227 | 0.6998580 |
| MMS1:Olaparib2: WT vs. ALC1 KO | -1.0456296 | 2.2395363 | -0.4668956 | 0.6412260 |
| MMS2:Olaparib2: WT vs. ALC1 KO | 2.2067138 | 2.2395363 | 0.9853441 | 0.3259809 |
| MMS1:Olaparib1: WT vs. ALC1 KO PARP1 KO | 5.2229965 | 2.2395363 | 2.3321777 | 0.0209695 |
| MMS2:Olaparib1: WT vs. ALC1 KO PARP1 KO | -0.4217179 | 2.2395363 | -0.1883059 | 0.8508816 |
| MMS1:Olaparib2: WT vs. ALC1 KO PARP1 KO | 0.2362858 | 2.2395363 | 0.1055066 | 0.9161094 |
| MMS2:Olaparib2: WT vs. ALC1 KO PARP1 KO | -0.2109463 | 2.2395363 | -0.0941920 | 0.9250775 |
| MMS1:Olaparib1: WT vs. PARP1 KO | 4.6766498 | 2.2395363 | 2.0882224 | 0.0384024 |
| MMS2:Olaparib1: WT vs. PARP1 KO | -3.2057139 | 2.2395363 | -1.4314186 | 0.1543106 |
| MMS1:Olaparib2: WT vs. PARP1 KO | 1.1063754 | 2.2395363 | 0.4940199 | 0.6219870 |
| MMS2:Olaparib2: WT vs. PARP1 KO | -0.4482443 | 2.2395363 | -0.2001505 | 0.8416235 |
write.table(output, file = "Figure3B_Stats_Ref_WT.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
# re-fit with ALC1KO reference
dataset$genotype <- relevel(dataset$genotype, ref = "ALC1 KO")
fit <- lm(NormCounts2 ~ poly(MMS,2)*poly(Olaparib,2)*genotype, data = dataset)
output <- coef(summary(fit))
output <- output[grep("MMS|Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype", paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1], sep = " in " )
# suggested result table
kable(output, row.names = T)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| MMS1 in ALC1 KO | -2.0807432 | 0.1142859 | -18.2064806 | 0.0000000 |
| MMS2 in ALC1 KO | 0.0386876 | 0.1142859 | 0.3385158 | 0.7354297 |
| Olaparib1 in ALC1 KO | -2.9831271 | 0.1142859 | -26.1023298 | 0.0000000 |
| Olaparib2 in ALC1 KO | 0.4438714 | 0.1142859 | 3.8838702 | 0.0001515 |
| MMS1:Olaparib1 in ALC1 KO | 14.8960620 | 1.5835913 | 9.4065067 | 0.0000000 |
| MMS2:Olaparib1 in ALC1 KO | -0.2764601 | 1.5835913 | -0.1745780 | 0.8616376 |
| MMS1:Olaparib2 in ALC1 KO | 0.5967985 | 1.5835913 | 0.3768640 | 0.7067868 |
| MMS2:Olaparib2 in ALC1 KO | 1.5566592 | 1.5835913 | 0.9829931 | 0.3271329 |
| MMS1: ALC1 KO vs. WT | 0.0986626 | 0.1616246 | 0.6104431 | 0.5424566 |
| MMS2: ALC1 KO vs. WT | 0.0868971 | 0.1616246 | 0.5376476 | 0.5915865 |
| MMS1: ALC1 KO vs. ALC1 KO PARP1 KO | -1.3014453 | 0.1616246 | -8.0522720 | 0.0000000 |
| MMS2: ALC1 KO vs. ALC1 KO PARP1 KO | 0.0274664 | 0.1616246 | 0.1699393 | 0.8652780 |
| MMS1: ALC1 KO vs. PARP1 KO | -1.1242551 | 0.1616246 | -6.9559649 | 0.0000000 |
| MMS2: ALC1 KO vs. PARP1 KO | -0.1025771 | 0.1616246 | -0.6346626 | 0.5265782 |
| Olaparib1: ALC1 KO vs. WT | 0.8335123 | 0.1616246 | 5.1570878 | 0.0000008 |
| Olaparib2: ALC1 KO vs. WT | -0.8008124 | 0.1616246 | -4.9547675 | 0.0000019 |
| Olaparib1: ALC1 KO vs. ALC1 KO PARP1 KO | 1.8027302 | 0.1616246 | 11.1538099 | 0.0000000 |
| Olaparib2: ALC1 KO vs. ALC1 KO PARP1 KO | -0.3261245 | 0.1616246 | -2.0177898 | 0.0453274 |
| Olaparib1: ALC1 KO vs. PARP1 KO | 1.8014335 | 0.1616246 | 11.1457871 | 0.0000000 |
| Olaparib2: ALC1 KO vs. PARP1 KO | -0.6659911 | 0.1616246 | -4.1206048 | 0.0000612 |
| MMS1:Olaparib1: ALC1 KO vs. WT | -17.6681357 | 2.2395363 | -7.8891939 | 0.0000000 |
| MMS2:Olaparib1: ALC1 KO vs. WT | 0.8649597 | 2.2395363 | 0.3862227 | 0.6998580 |
| MMS1:Olaparib2: ALC1 KO vs. WT | 1.0456296 | 2.2395363 | 0.4668956 | 0.6412260 |
| MMS2:Olaparib2: ALC1 KO vs. WT | -2.2067138 | 2.2395363 | -0.9853441 | 0.3259809 |
| MMS1:Olaparib1: ALC1 KO vs. ALC1 KO PARP1 KO | -12.4451392 | 2.2395363 | -5.5570162 | 0.0000001 |
| MMS2:Olaparib1: ALC1 KO vs. ALC1 KO PARP1 KO | 0.4432418 | 2.2395363 | 0.1979168 | 0.8433678 |
| MMS1:Olaparib2: ALC1 KO vs. ALC1 KO PARP1 KO | 1.2819153 | 2.2395363 | 0.5724021 | 0.5678736 |
| MMS2:Olaparib2: ALC1 KO vs. ALC1 KO PARP1 KO | -2.4176601 | 2.2395363 | -1.0795360 | 0.2820151 |
| MMS1:Olaparib1: ALC1 KO vs. PARP1 KO | -12.9914859 | 2.2395363 | -5.8009714 | 0.0000000 |
| MMS2:Olaparib1: ALC1 KO vs. PARP1 KO | -2.3407542 | 2.2395363 | -1.0451960 | 0.2975493 |
| MMS1:Olaparib2: ALC1 KO vs. PARP1 KO | 2.1520050 | 2.2395363 | 0.9609155 | 0.3380817 |
| MMS2:Olaparib2: ALC1 KO vs. PARP1 KO | -2.6549581 | 2.2395363 | -1.1854946 | 0.2376246 |
write.table(output, file = "Figure3B_Stats_Ref_ALC1.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
# re-fit with ALC1 KO PARP1 KO reference
dataset$genotype <- relevel(dataset$genotype, ref = "ALC1 KO PARP1 KO ")
fit <- lm(NormCounts2 ~ poly(MMS,2)*poly(Olaparib,2)*genotype, data = dataset)
output <- coef(summary(fit))
output <- output[grep("MMS|Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype", paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1], sep = " in " )
# suggested result table
kable(output, row.names = T)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| MMS1 in ALC1 KO PARP1 KO | -3.3821885 | 0.1142859 | -29.5941129 | 0.0000000 |
| MMS2 in ALC1 KO PARP1 KO | 0.0661539 | 0.1142859 | 0.5788463 | 0.5635277 |
| Olaparib1 in ALC1 KO PARP1 KO | -1.1803969 | 0.1142859 | -10.3284605 | 0.0000000 |
| Olaparib2 in ALC1 KO PARP1 KO | 0.1177470 | 0.1142859 | 1.0302845 | 0.3044712 |
| MMS1:Olaparib1 in ALC1 KO PARP1 KO | 2.4509228 | 1.5835913 | 1.5476991 | 0.1237210 |
| MMS2:Olaparib1 in ALC1 KO PARP1 KO | 0.1667817 | 1.5835913 | 0.1053186 | 0.9162583 |
| MMS1:Olaparib2 in ALC1 KO PARP1 KO | 1.8787139 | 1.5835913 | 1.1863628 | 0.2372827 |
| MMS2:Olaparib2 in ALC1 KO PARP1 KO | -0.8610009 | 1.5835913 | -0.5437015 | 0.5874231 |
| MMS1: ALC1 KO PARP1 KO vs. ALC1 KO | 1.3014453 | 0.1616246 | 8.0522720 | 0.0000000 |
| MMS2: ALC1 KO PARP1 KO vs. ALC1 KO | -0.0274664 | 0.1616246 | -0.1699393 | 0.8652780 |
| MMS1: ALC1 KO PARP1 KO vs. WT | 1.4001079 | 0.1616246 | 8.6627151 | 0.0000000 |
| MMS2: ALC1 KO PARP1 KO vs. WT | 0.0594307 | 0.1616246 | 0.3677083 | 0.7135892 |
| MMS1: ALC1 KO PARP1 KO vs. PARP1 KO | 0.1771902 | 0.1616246 | 1.0963072 | 0.2746339 |
| MMS2: ALC1 KO PARP1 KO vs. PARP1 KO | -0.1300435 | 0.1616246 | -0.8046019 | 0.4222739 |
| Olaparib1: ALC1 KO PARP1 KO vs. ALC1 KO | -1.8027302 | 0.1616246 | -11.1538099 | 0.0000000 |
| Olaparib2: ALC1 KO PARP1 KO vs. ALC1 KO | 0.3261245 | 0.1616246 | 2.0177898 | 0.0453274 |
| Olaparib1: ALC1 KO PARP1 KO vs. WT | -0.9692179 | 0.1616246 | -5.9967221 | 0.0000000 |
| Olaparib2: ALC1 KO PARP1 KO vs. WT | -0.4746879 | 0.1616246 | -2.9369777 | 0.0038166 |
| Olaparib1: ALC1 KO PARP1 KO vs. PARP1 KO | -0.0012967 | 0.1616246 | -0.0080229 | 0.9936090 |
| Olaparib2: ALC1 KO PARP1 KO vs. PARP1 KO | -0.3398666 | 0.1616246 | -2.1028150 | 0.0370872 |
| MMS1:Olaparib1: ALC1 KO PARP1 KO vs. ALC1 KO | 12.4451392 | 2.2395363 | 5.5570162 | 0.0000001 |
| MMS2:Olaparib1: ALC1 KO PARP1 KO vs. ALC1 KO | -0.4432418 | 2.2395363 | -0.1979168 | 0.8433678 |
| MMS1:Olaparib2: ALC1 KO PARP1 KO vs. ALC1 KO | -1.2819153 | 2.2395363 | -0.5724021 | 0.5678736 |
| MMS2:Olaparib2: ALC1 KO PARP1 KO vs. ALC1 KO | 2.4176601 | 2.2395363 | 1.0795360 | 0.2820151 |
| MMS1:Olaparib1: ALC1 KO PARP1 KO vs. WT | -5.2229965 | 2.2395363 | -2.3321777 | 0.0209695 |
| MMS2:Olaparib1: ALC1 KO PARP1 KO vs. WT | 0.4217179 | 2.2395363 | 0.1883059 | 0.8508816 |
| MMS1:Olaparib2: ALC1 KO PARP1 KO vs. WT | -0.2362858 | 2.2395363 | -0.1055066 | 0.9161094 |
| MMS2:Olaparib2: ALC1 KO PARP1 KO vs. WT | 0.2109463 | 2.2395363 | 0.0941920 | 0.9250775 |
| MMS1:Olaparib1: ALC1 KO PARP1 KO vs. PARP1 KO | -0.5463467 | 2.2395363 | -0.2439553 | 0.8075860 |
| MMS2:Olaparib1: ALC1 KO PARP1 KO vs. PARP1 KO | -2.7839960 | 2.2395363 | -1.2431127 | 0.2156914 |
| MMS1:Olaparib2: ALC1 KO PARP1 KO vs. PARP1 KO | 0.8700897 | 2.2395363 | 0.3885133 | 0.6981659 |
| MMS2:Olaparib2: ALC1 KO PARP1 KO vs. PARP1 KO | -0.2372980 | 2.2395363 | -0.1059586 | 0.9157514 |
write.table(output, file = "Figure3B_Stats_Ref_ALC1_PARP1.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
# re-fit with PARP1 KO reference
dataset$genotype <- relevel(dataset$genotype, ref = "PARP1 KO")
fit <- lm(NormCounts2 ~ poly(MMS,2)*poly(Olaparib,2)*genotype, data = dataset)
output <- coef(summary(fit))
output <- output[grep("MMS|Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype", paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1], sep = " in " )
# suggested result table
kable(output, row.names = T)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| MMS1 in PARP1 KO | -3.2049983 | 0.1142859 | -28.0437005 | 0.0000000 |
| MMS2 in PARP1 KO | -0.0638895 | 0.1142859 | -0.5590327 | 0.5769410 |
| Olaparib1 in PARP1 KO | -1.1816936 | 0.1142859 | -10.3398065 | 0.0000000 |
| Olaparib2 in PARP1 KO | -0.2221197 | 0.1142859 | -1.9435449 | 0.0537502 |
| MMS1:Olaparib1 in PARP1 KO | 1.9045761 | 1.5835913 | 1.2026942 | 0.2309169 |
| MMS2:Olaparib1 in PARP1 KO | -2.6172144 | 1.5835913 | -1.6527082 | 0.1004015 |
| MMS1:Olaparib2 in PARP1 KO | 2.7488035 | 1.5835913 | 1.7358037 | 0.0845731 |
| MMS2:Olaparib2 in PARP1 KO | -1.0982989 | 1.5835913 | -0.6935495 | 0.4889961 |
| MMS1: PARP1 KO vs. ALC1 KO PARP1 KO | -0.1771902 | 0.1616246 | -1.0963072 | 0.2746339 |
| MMS2: PARP1 KO vs. ALC1 KO PARP1 KO | 0.1300435 | 0.1616246 | 0.8046019 | 0.4222739 |
| MMS1: PARP1 KO vs. ALC1 KO | 1.1242551 | 0.1616246 | 6.9559649 | 0.0000000 |
| MMS2: PARP1 KO vs. ALC1 KO | 0.1025771 | 0.1616246 | 0.6346626 | 0.5265782 |
| MMS1: PARP1 KO vs. WT | 1.2229177 | 0.1616246 | 7.5664079 | 0.0000000 |
| MMS2: PARP1 KO vs. WT | 0.1894742 | 0.1616246 | 1.1723102 | 0.2428597 |
| Olaparib1: PARP1 KO vs. ALC1 KO PARP1 KO | 0.0012967 | 0.1616246 | 0.0080229 | 0.9936090 |
| Olaparib2: PARP1 KO vs. ALC1 KO PARP1 KO | 0.3398666 | 0.1616246 | 2.1028150 | 0.0370872 |
| Olaparib1: PARP1 KO vs. ALC1 KO | -1.8014335 | 0.1616246 | -11.1457871 | 0.0000000 |
| Olaparib2: PARP1 KO vs. ALC1 KO | 0.6659911 | 0.1616246 | 4.1206048 | 0.0000612 |
| Olaparib1: PARP1 KO vs. WT | -0.9679212 | 0.1616246 | -5.9886992 | 0.0000000 |
| Olaparib2: PARP1 KO vs. WT | -0.1348212 | 0.1616246 | -0.8341628 | 0.4054648 |
| MMS1:Olaparib1: PARP1 KO vs. ALC1 KO PARP1 KO | 0.5463467 | 2.2395363 | 0.2439553 | 0.8075860 |
| MMS2:Olaparib1: PARP1 KO vs. ALC1 KO PARP1 KO | 2.7839960 | 2.2395363 | 1.2431127 | 0.2156914 |
| MMS1:Olaparib2: PARP1 KO vs. ALC1 KO PARP1 KO | -0.8700897 | 2.2395363 | -0.3885133 | 0.6981659 |
| MMS2:Olaparib2: PARP1 KO vs. ALC1 KO PARP1 KO | 0.2372980 | 2.2395363 | 0.1059586 | 0.9157514 |
| MMS1:Olaparib1: PARP1 KO vs. ALC1 KO | 12.9914859 | 2.2395363 | 5.8009714 | 0.0000000 |
| MMS2:Olaparib1: PARP1 KO vs. ALC1 KO | 2.3407542 | 2.2395363 | 1.0451960 | 0.2975493 |
| MMS1:Olaparib2: PARP1 KO vs. ALC1 KO | -2.1520050 | 2.2395363 | -0.9609155 | 0.3380817 |
| MMS2:Olaparib2: PARP1 KO vs. ALC1 KO | 2.6549581 | 2.2395363 | 1.1854946 | 0.2376246 |
| MMS1:Olaparib1: PARP1 KO vs. WT | -4.6766498 | 2.2395363 | -2.0882224 | 0.0384024 |
| MMS2:Olaparib1: PARP1 KO vs. WT | 3.2057139 | 2.2395363 | 1.4314186 | 0.1543106 |
| MMS1:Olaparib2: PARP1 KO vs. WT | -1.1063754 | 2.2395363 | -0.4940199 | 0.6219870 |
| MMS2:Olaparib2: PARP1 KO vs. WT | 0.4482443 | 2.2395363 | 0.2001505 | 0.8416235 |
write.table(output, file = "Figure3B_Stats_Ref_PARP1.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
fit7a <- lm(NormCounts2 ~ poly(MMS,2)*poly(Olaparib,2)*genotype, data = dataset)
fit7b <- lm(NormCounts2 ~ poly(MMS,2)*poly(Olaparib,2)+genotype, data = dataset)
# anova table
anova(fit7a, fit7b)
## Analysis of Variance Table
##
## Model 1: NormCounts2 ~ poly(MMS, 2) * poly(Olaparib, 2) * genotype
## Model 2: NormCounts2 ~ poly(MMS, 2) * poly(Olaparib, 2) + genotype
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 156 0.50939
## 2 180 1.82105 -24 -1.3117 16.737 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1